Methods of assessing risk of developing a severe response to coronavirus infection

ABSTRACT

The present disclosure relates to methods and systems for assessing the risk of a human subject developing a severe response to a Coronavirus infection, such as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus infection.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of PCT International Application No. PCT/AU2021/050507, filed May 26, 2021, which claims the priority of each of Australian Application No. 2020901739, filed May 27, 2020, Australian Application No. 2020902052, filed Jun. 19, 2020, Australian Application No. 2020903536, filed Sep. 30, 2020, and Australian Application No. 2021900392, filed Feb. 17, 2021 the contents of each of which are hereby incorporated by reference in their entirety into this application.

REFERENCE TO SEQUENCE LISTING

This application incorporates-by-reference nucleotide and/or amino acid sequences which are present in the file named “210706_91753_SequenceListing_DH.txt”, which is 4 kilobytes in size, and which was created Jul. 5, 2021 in the IBM-PC machine format, having an operating system compatibility with MS-Windows, which is contained in the text file filed Jul. 6, 2021 as part of this application.

FIELD OF THE INVENTION

The present disclosure relates to methods and systems for assessing the risk of a human subject developing a severe response to a coronavirus infection such as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral infection.

BACKGROUND OF THE INVENTION

In December 2019, there were a series of unexplained cases of pneumonia reported in Wuhan, China. On 12 Jan. 2020, the World Health Organization (WHO) tentatively named this new virus as the 2019 novel coronavirus (2019-nCoV). On 11 Feb. 2020, the WHO formally named the disease triggered by 2019-nCoV as coronavirus disease 2019 (COVID-19). The coronavirus study group of the International Committee on Taxonomy of Viruses named 2019-nCoV as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The WHO declared the virus a Public Health Emergency of International Concern on 30 Jan. 2020. The WHO eventually declared a pandemic on 11 Mar. 2020.

Like many complex diseases, there are a multitude of host factors that influence the severity of disease once infected with a virus. This means viral infections are complex multifactorial diseases like many cancers, cardiovascular disease and diabetes.

As global health systems try to manage resources and governments attempt to manage their respective economies there is a need to identify which people are at most risk of developing severe symptoms in response to the viral infection. Such a tool would enable earlier hospitalization and targeted treatments which may lead to the saving of lives. Of great importance to the economy, there is potential that lower risk individuals could be recommended to continue their normal employment given the lower risk of developing a life threatening disease should they contract a Coronavirus infection such as a SARS-Cov-2 viral infection.

SUMMARY OF THE INVENTION

The present inventors have found that a severe response to a Coronavirus infection risk model provides useful risk discrimination for assessing a subject's risk of developing a severe response to a Coronavirus infection such as a SARS-CoV-2 infection.

In an aspect, the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising performing a genetic risk assessment of the human subject, wherein the genetic risk assessment involves detecting, in a biological sample derived from the human subject, the presence at least two polymorphisms associated with a severe response to a Coronavirus infection.

In an embodiment, the Coronavirus is an Alphacoronavirus, Betacoronavirus, Gammacoronavirus or an Deltacoronavirus.

In an embodiment, the Coronavirus is Alphacoronavirus 1, Human coronavirus 229E, Human coronavirus NL63, Miniopterus bat coronavirus 1, Miniopterus bat coronavirus HKU8, Porcine epidemic diarrhea virus, Rhinolophus bat coronavirus HKU2, Scotophilus bat coronavirus 512, Betacoronavirus 1 (Bovine Coronavirus, Human coronavirus OC43), Hedgehog coronavirus 1, Human coronavirus HKU1, Middle East respiratory syndrome-related coronavirus (MERS), Murine coronavirus, Pipistrellus bat coronavirus HKU5, Rousettus bat coronavirus HKU9, Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2), Tylonycteris bat coronavirus HKU4, Avian coronavirus, Beluga whale coronavirus SW1, Bulbul coronavirus HKU11 or Porcine coronavirus HKU15.

In an embodiment, the Coronavirus is Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2), Middle East respiratory syndrome-related coronavirus (MERS), Human coronavirus OC43, Human coronavirus HKU1, Human coronavirus 229E or Human coronavirus NL63.

In an embodiment, the Coronavirus is a Betacoronavirus.

In an embodiment, the Betacoronavirus is Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2), Middle East respiratory syndrome-related coronavirus (MERS), Human coronavirus OC43 or Human coronavirus HKU1.

In an embodiment, the Coronavirus (Betacoronavirus) is Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2), Middle East respiratory syndrome-related coronavirus (MERS), Human coronavirus OC43 or Human coronavirus HKU1.

In an embodiment, the Coronavirus (Betacoronavirus) is Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2) or Middle East respiratory syndrome-related coronavirus (MERS).

In a preferred embodiment, the Coronavirus (Betacoronavirus) is Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

In an embodiment, the method comprises detecting the presence of at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 polymorphisms associated with a severe response to a Coronavirus infection.

In an embodiment, the polymorphisms are selected from Tables 1 to 6, 8, 19 or 22 or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the method at least comprises detecting polymorphisms at one or more or all of rs10755709, rs112317747, rs112641600, rs118072448, rs2034831, rs7027911 and rs71481792, or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the method at least comprises detecting polymorphisms at one or more or all of rs10755709, rs112317747, rs112641600, rs115492982, rs118072448, rs1984162, rs2034831, rs7027911 and rs71481792, or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the polymorphisms are selected from Table 1, Table 6a, Table 6b or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the polymorphisms are selected from any one of Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the polymorphisms are selected from Table 3 or a polymorphism in linkage disequilibrium with one or more thereof. In an embodiment, at least three polymorphisms are analysed.

In an embodiment, the method comprises, or consists of, detecting the presence of at least 60, or each, of the polymorphisms provided in Table 4 or a polymorphism in linkage disequilibrium with one or more thereof.

In another embodiment, the polymorphisms are selected from Table 2 or a polymorphism in linkage disequilibrium with one or more thereof.

In a further embodiment, the polymorphisms are selected from Table 3 and/or Table 8 or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the polymorphisms are selected from Table 3 or a polymorphism in linkage disequilibrium with one or more thereof. In an embodiment, the method comprises, or consists of, detecting the presence of each of the polymorphisms provided in Table 3 or a polymorphism in linkage disequilibrium with one or more thereof.

The genetic risk assessment may be combined with clinical risk factors to further improve the risk analysis. Thus, in an embodiment, the method further comprises

performing a clinical risk assessment of the human subject; and

combining the clinical risk assessment and the genetic risk assessment to obtain the risk of a human subject developing a severe response to a Coronavirus infection.

In an embodiment, the clinical risk assessment includes obtaining information from the subject on, but not necessarily limited to, one or more of the following: age, family history of a severe response to a Coronavirus infection, race/ethnicity, gender, body mass index, total cholesterol level, systolic and/or diastolic blood pressure, smoking status, does the human have diabetes, does the human have a cardiovascular disease, is the subject on hypertension medication, loss of taste, loss of smell and white blood cell count.

In another embodiment, the clinical risk assessment is based only on one or more or all of age, body mass index, loss of taste, loss of smell and smoking status.

In a further embodiment, the clinical risk assessment is based only on one or more or all of age, loss of taste, loss of smell and smoking status.

In an embodiment, the clinical risk assessment includes obtaining information from the subject on one or more or all of: age, gender, race/ethnicity, blood type, does the human have or has had an autoimmune disease, does the human have or has had an haematological cancer, does the human have or has had an non-haematological cancer, does the human have or has had diabetes, does the human have or has had hypertension and does the human have or has had a respiratory disease (other than asthma). In an embodiment, the autoimmune disease is rheumatoid arthritis, lupus or psoriasis.

In an embodiment, the clinical risk assessment includes obtaining information from the subject on one or more or all of: age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).

The skilled person would appreciate that numerous different procedures can be followed to combine the clinical and genetic risk assessments. In an embodiment, combining the clinical risk assessment and the genetic risk assessment comprises multiplying the risk assessments. In an embodiment, combining the clinical risk assessment and the genetic risk assessment comprises adding the risk assessments.

The inventors, for the first time, have identified numerous polymorphisms associated with a subject's risk of developing a severe response to a Coronavirus infection. Thus, in another aspect, the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus, the method comprising detecting, in a biological sample derived from the human subject, the presence of a polymorphism provided in any one of Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium therewith.

In an embodiment, the polymorphism is provided in Table 19 and/or 22 or is a polymorphism in linkage disequilibrium therewith.

In an embodiment, the polymorphism is provided in Table 1 or Table 6a or is a polymorphism in linkage disequilibrium therewith.

In an embodiment, the polymorphism is provided in Table 3 or Table 6a or is a polymorphism in linkage disequilibrium therewith.

In an embodiment, the polymorphism is provided in Table 3, Table 6, is rs2274122, is rs1868132, is rs11729561, is rs1984162, is rs8105499 or is a polymorphism in linkage disequilibrium therewith.

In an embodiment, the polymorphism is provided in Table 3, is rs2274122, is rs1868132, is rs11729561, is rs1984162, is rs8105499 or is a polymorphism in linkage disequilibrium therewith.

In another aspect, the present invention provides a method of determining the identity of the alleles of fewer than 100,000 polymorphisms in a human subject selected from the group of subjects consisting of humans in need of assessment for the risk of developing a severe response to a Coronavirus infection to produce a polymorphic profile of the subject, comprising

(i) selecting for allelic identity analysis at least two polymorphisms provided in any one of Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium with one or more thereof,

(ii) detecting, in a biological sample derived from the human subject, the polymorphisms, and

(iii) producing the polymorphic profile of the subject screening based on the identity of the alleles analysed in step (ii), wherein fewer than 100,000 polymorphisms are selected for allelic identity analysis in step (i) and the same fewer than 100,000 polymorphisms are analysed in step (ii).

In an embodiment of the above aspect, fewer than 100,000 polymorphisms, fewer than 50,000 polymorphisms, fewer than 40,000 polymorphisms, fewer than 30,000 polymorphisms, fewer than 20,000 polymorphisms, fewer than 10,000 polymorphisms, fewer than 7,500 polymorphisms, fewer than 5,000 polymorphisms, fewer than 4,000 polymorphisms, fewer than 3,000 polymorphisms, fewer than 2,000 polymorphisms, fewer than 1,000 polymorphisms, fewer than 900 polymorphisms, fewer than 800 polymorphisms, fewer than 700 polymorphisms, fewer than 600 polymorphisms, fewer than 500 polymorphisms, fewer than 400 polymorphisms, fewer than 300 polymorphisms, fewer than 200 polymorphisms, or fewer than 100 polymorphisms, are selected for allelic identity.

In an embodiment of each of the above aspects, the human subject can be Caucasian, African American, Hispanic, Asian, Indian, or Latino. In a preferred embodiment, the human subject is Caucasian.

In an embodiment of each of the above aspects, the method further comprises obtaining the biological sample.

In an embodiment, the polymorphism(s) in linkage disequilibrium has linkage disequilibrium above 0.9. In another embodiment, the polymorphism(s) in linkage disequilibrium has linkage disequilibrium of 1.

The present inventors have also found that a severe response to a Coronavirus infection risk model that relies solely on clinical factors provides useful risk discrimination for assessing a subject's risk of developing a severe response to a Coronavirus infection such as a SARS-CoV-2 infection. Such a test may be particularly useful in circumstances where a rapid decision needs to be made and/or when genetic testing is not readily available. Thus, in another aspect the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising performing a clinical risk assessment of the human subject, wherein the clinical risk assessment comprises obtaining information from the subject on two, three, four, five or more or all of age, gender, race/ethnicity, height, weight, blood type, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had an autoimmune disease, does the human have or has had an haematological cancer, does the human have or has had an immunocompromised disease, does the human have or has had an non-haematological cancer, does the human have or has had diabetes, does the human have or has had liver disease, does the human have or has had hypertension and does the human have or has had a respiratory disease (other than asthma).

In an embodiment, the method comprises obtaining information from the subject on age and gender.

In an embodiment, the method comprises obtaining information from the subject on age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).

In an embodiment, the method comprises obtaining information from the subject on age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).

In an embodiment, the method comprises obtaining information from the subject on one or more of all of age, gender, race/ethnicity, blood type, does the human have or has had an autoimmune disease, does the human have or has had an haematological cancer, does the human have or has had an non-haematological cancer, does the human have or has had diabetes, does the human have or has had hypertension and does the human have or has had a respiratory disease (other than asthma).

In another aspect, the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising

i) performing a genetic risk assessment of the human subject, wherein the genetic risk assessment involves detecting, in a biological sample derived from the human subject, polymorphisms at rs10755709, rs112317747, rs112641600, rs118072448, rs2034831, rs7027911 and rs71481792,

ii) performing a clinical risk assessment of the human subject, wherein the clinical risk assessment comprises obtaining information from the subject on age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma), and

iii) combining the genetic risk assessment with the clinical risk assessment to determine the risk of a human subject developing a severe response to a Coronavirus infection.

In an embodiment,

a) a β coefficient of 0.124239 is assigned for each G allele at rs10755709;

b) a β coefficient of 0.2737487 is assigned for each C allele at rs112317747;

c) a β coefficient of −0.2362513 is assigned for each T allele at rs112641600;

d) a β coefficient of −0.1995879 is assigned for each C allele at rs118072448;

e) a β coefficient of 0.2371955 is assigned for each C allele at rs2034831;

f) a β coefficient of 0.1019074 is assigned for each A allele at rs7027911; and

g) a β coefficient of −0.1058025 is assigned for each T allele at rs71481792.

In an embodiment, the subject is between 50 and 84 years of age and

a) a β coefficient of 0.5747727 is assigned if the subject is between 70 and 74 years of age;

b) a β coefficient of 0.8243711 is assigned if the subject is between 75 and 79 years of age;

c) a β coefficient of 1.013973 is assigned if the subject is between 80 and 84 years of age;

d) a β coefficient of 0.2444891 is assigned if the subject is male;

e) a β coefficient of 0.29311 is assigned if the subject is an ethnicity other than Caucasian;

f) the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m²) divided by kg, which is multiplied by −1.602056 to provide the β coefficient to be assigned;

g) a β coefficient of 0.4041337 is assigned if the subject has ever been diagnosed as having a cerebrovascular disease;

h) a β coefficient of 0.6938494 is assigned if the subject has ever been diagnosed as having a chronic kidney disease;

i) a β coefficient of 0.4297612 is assigned if the subject has ever been diagnosed as having diabetes;

j) a β coefficient of 1.003877 is assigned if the subject has ever been diagnosed as having haematological cancer;

k) a β coefficient of 0.2922307 is assigned if the subject has ever been diagnosed as having hypertension;

l) a β coefficient of 0.2558464 is assigned if the subject has ever been diagnosed as having a non-haematological cancer; and

m) a β coefficient of 1.173753 is assigned if the subject has ever been diagnosed as having a respiratory disease (other than asthma).

In an embodiment, the subject is between 18 and 49 years of age and

a) a β coefficient of −1.3111 is assigned if the subject is between 18 and 29 years of age;

b) a β coefficient of −0.8348 is assigned if the subject is between 30 and 39 years of age;

c) a β coefficient of −0.4038 is assigned if the subject is between 40 and 49 years of age;

d) a β coefficient of 0.2444891 is assigned if the subject is male;

e) a β coefficient of 0.29311 is assigned if the subject is an ethnicity other than Caucasian;

f) the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m²) divided by kg, which is multiplied by −1.602056 to provide the β coefficient to be assigned;

g) a β coefficient of 0.4041337 is assigned if the subject has ever been diagnosed as having a cerebrovascular disease;

h) a β coefficient of 0.6938494 is assigned if the subject has ever been diagnosed as having a chronic kidney disease;

i) a β coefficient of 0.4297612 is assigned if the subject has ever been diagnosed as having diabetes;

j) a β coefficient of 1.003877 is assigned if the subject has ever been diagnosed as having haematological cancer;

k) a β coefficient of 0.2922307 is assigned if the subject has ever been diagnosed as having hypertension;

l) a β coefficient of 0.2558464 is assigned if the subject has ever been diagnosed as having a non-haematological cancer; and

m) a β coefficient of 1.173753 is assigned if the subject has ever been diagnosed as having a respiratory disease (other than asthma).

In an embodiment, the subject is between 18 and 84 years of age and

a) a β coefficient of −1.3111 is assigned if the subject is between 18 and 29 years of age;

b) a β coefficient of −0.8348 is assigned if the subject is between 30 and 39 years of age;

c) a β coefficient of −0.4038 is assigned if the subject is between 40 and 49 years of age;

d) a β coefficient of 0.5747727 is assigned if the subject is between 70 and 74 years of age;

e) a β coefficient of 0.8243711 is assigned if the subject is between 75 and 79 years of age;

f) a β coefficient of 1.013973 is assigned if the subject is between 80 and 84 years of age;

g) a β coefficient of 0.2444891 is assigned if the subject is male;

h) a β coefficient of 0.29311 is assigned if the subject is an ethnicity other than Caucasian;

i) the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m²) divided by kg, which is multiplied by −1.602056 to provide the β coefficient to be assigned;

j) a β coefficient of 0.4041337 is assigned if the subject has ever been diagnosed as having a cerebrovascular disease;

k) a β coefficient of 0.6938494 is assigned if the subject has ever been diagnosed as having a chronic kidney disease;

l) a β coefficient of 0.4297612 is assigned if the subject has ever been diagnosed as having diabetes;

m) a β coefficient of 1.003877 is assigned if the subject has ever been diagnosed as having haematological cancer;

n) a β coefficient of 0.2922307 is assigned if the subject has ever been diagnosed as having hypertension;

o) a β coefficient of 0.2558464 is assigned if the subject has ever been diagnosed as having a non-haematological cancer; and

p) a β coefficient of 1.173753 is assigned if the subject has ever been diagnosed as having a respiratory disease (other than asthma).

In an embodiment, in step iii) the genetic risk assessment is combined with the clinical risk assessment using the following formula:

Long Odds (LO)=−1.36523+SRF+Σ Clinical β coefficients, and wherein SRF is the SNP Risk Factor which is determined using the following formula:

Σ(No of Risk Alleles×SNP β coefficient).

In another aspect, the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising

i) performing a genetic risk assessment of the human subject, wherein the genetic risk assessment involves detecting, in a biological sample derived from the human subject, polymorphisms at rs10755709, rs112317747, rs112641600, rs115492982, rs118072448, rs1984162, rs2034831, rs7027911 and rs71481792,

ii) performing a clinical risk assessment of the human subject, wherein the clinical risk assessment comprises obtaining information from the subject of age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma), and

iii) combining the genetic risk assessment with the clinical risk assessment to determine the risk of a human subject developing a severe response to a Coronavirus infection.

In an embodiment,

a) a β coefficient of 0.1231766 is assigned for each G allele at rs10755709;

b) a β coefficient of 0.2576692 is assigned for each C allele at rs112317747;

c) a β coefficient of −0.2384001 is assigned for each T allele at rs112641600;

d) a β coefficient of −0.1965609 is assigned for each C allele at rs118072448;

e) a β coefficient of 0.2414792 is assigned for each C allele at rs2034831;

f) a β coefficient of 0.0998459 is assigned for each A allele at rs7027911;

g) a β coefficient of −0.1032044 is assigned for each T allele at rs71481792;

h) a β coefficient of 0.4163575 is assigned for each A allele at rs115492982; and

i) a β coefficient of 0.1034362 is assigned for each A allele at rs1984162.

In a further embodiment, the subject is between 50 and 84 years of age and

a) a β coefficient of 0.1677566 is assigned if the subject is between 65 and 69 years of age;

b) a β coefficient of 0.6352682 is assigned if the subject is between 70 and 74 years of age;

c) a β coefficient of 0.8940548 is assigned if the subject is between 75 and 79 years of age;

d) a β coefficient of 1.082477 is assigned if the subject is between 80 and 84 years of age;

e) a β coefficient of 0.2418454 is assigned if the subject is male;

f) a β coefficient of 0.2967777 is assigned if the subject is an ethnicity other than Caucasian;

g) the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m²) divided by kg, which is multiplied by −1.560943 to provide the β coefficient to be assigned;

h) a β coefficient of 0.3950113 is assigned if the subject has ever been diagnosed as having a cerebrovascular disease;

i) a β coefficient of 0.6650257 is assigned if the subject has ever been diagnosed as having a chronic kidney disease;

j) a β coefficient of 0.4126633 is assigned if the subject has ever been diagnosed as having diabetes;

k) a β coefficient of 1.001079 is assigned if the subject has ever been diagnosed as having haematological cancer;

l) a β coefficient of 0.2640989 is assigned if the subject has ever been diagnosed as having hypertension;

m) a β coefficient of 0.2381579 is assigned if the subject has ever been diagnosed as having a non-haematological cancer;

n) a β coefficient of 1.148496 is assigned if the subject has ever been diagnosed as having a respiratory disease (other than asthma);

o) a β coefficient of −0.229737 is assigned if the subject has an ABO blood type;

p) a β coefficient of 0.6033541 is assigned if the subject has ever been diagnosed as having a immunocompromised disease;

q) a β coefficient of 0.2301902 is assigned if the subject has ever been diagnosed as having liver disease.

In an embodiment, in step iii) the genetic risk assessment is combined with the clinical risk assessment using the following formula:

Long Odds (LO)=1.469939+SRF+Σ Clinical β coefficients,

and wherein SRF is the SNP Risk Factor which is determined using the following formula:

Σ(No of Risk Alleles×SNP β coefficient).

In an embodiment, a method of the invention further comprises determining the probability the subject would require hospitalisation if infected with a Coronavirus using the following formula:

e ^(LO)/(1+e ^(LO)),

which is then multiplied by 100 to obtain a percent chance of hospitalisation being required.

In an embodiment of each of the above aspects, the risk assessment produces a score and the method further comprises comparing the score to a predetermined threshold, wherein if the score is at, or above, the threshold the subject is assessed at being at risk of developing a severe response to a Coronavirus infection.

In an embodiment, if it is determined the subject has a risk of developing a severe response to a Coronavirus infection, the subject is more likely than someone assessed as low risk, or when compared to the average risk in the population, to be admitted to hospital for intensive care.

In a further aspect, the present invention provides a method for determining the need for routine diagnostic testing of a human subject for a Coronavirus infection comprising assessing the risk of the subject for developing a severe response to a Coronavirus infection using a method of the invention.

In another aspect, the present invention provides a method of screening for a severe response to a Coronavirus infection in a human subject, the method comprising assessing the risk of the subject for developing a severe response to a Coronavirus infection using a method of the invention, and routinely screening for a Coronavirus infection in the subject if they are assessed as having a risk for developing a severe response to a Coronavirus infection.

In an embodiment of the above two aspects, the screening involves analysing the subject for the virus or a symptom thereof.

In a further aspect, the present invention provides a method for determining the need of a human subject for prophylactic anti-Coronavirus therapy comprising assessing the risk of the subject for developing a severe response to a Coronavirus infection using a method of the invention.

In yet another aspect, the present invention provides a method for preventing or reducing the risk of a severe response to a Coronavirus infection in a human subject, the method comprising assessing the risk of the subject for developing a severe response to a Coronavirus infection using a method of the invention, and if they are assessed as having a risk for developing a severe response to a Coronavirus infection

1) administering an anti-Coronavirus therapy and/or

2) isolating the subject.

In an aspect, the present invention provides an anti-Coronavirus infection therapy for use in preventing a severe response to a Coronavirus infection in a human subject at risk thereof, wherein the subject is assessed as having a risk for developing a severe response to a Coronavirus infection using a method of the invention.

Many anti-Coronavirus therapies, such as anti-SARS-CoV-2 virus therapies, are in development. The skilled person would appreciate that any therapy shown to be successful can be used in the above methods. Possible examples include, but are not limited to, intubation to assist breathing, an anti-Coronavirus—such as anti-SARS-CoV-2 virus—vaccine, convalescent plasma (plasma from people who have been infected, developed antibodies to the virus, and have then recovered), chloroquine, hydroxychloroquine (with or without zinc), Favipiravir, Remdesivir, Ivermectin, Quercetin, Kaletra (lopinavir/ritonavir), Arbidol, Baricitinib, CM4620-IE, an IL-6 inhibitor, Tocilizumab and stem cells such as mesenchymal stem cells. In another embodiment, the therapy is Vitamin D. Other examples of therapy include, Dexamethasone (or other corticosteroids such as prednisone, methylprednisolone, or hydrocortisone), Baricitinib in combination with remdesivir, anticoagulation drugs (“blood thinners”), bamlanivimab and etesevimab, convalescent plasma, tocilizumab with corticosteroids, Casirivimab and Imdevimab, Atorvastatin, GRP78 and siRNA-nanoparticle formulations.

Once a vaccine (or indeed possibly many different anti-Coronavirus therapies) is developed it is highly likely there will be supply issues and decisions will need to be made about why one person will receive the vaccine first when compared to another person. The present invention can thus be used to determine who is at most risk, and the anti-Coronavirus therapy (such as a vaccine) first administered to people assessed as likely to develop a severe response to a Coronavirus infection.

In an embodiment, the vaccine is an mRNA vaccine. In an embodiment, the vaccine is a protein vaccine. Examples of vaccines that can be administered include, but are not limited to, the Pfizer-BioNTech vaccine, the Moderna vaccine, the Johnson & Johnson vaccine, the Oxford-AstraZeneca vaccine and the Novavax vaccine (see, for example, Katella, 2021).

In another embodiment, the present invention provides a method for stratifying a group of human subjects for a clinical trial of a candidate therapy, the method comprising assessing the individual risk of the subjects for developing a severe response to a Coronavirus infection using a method of the invention, and using the results of the assessment to select subjects more likely to be responsive to the therapy.

Also provided is a kit comprising at least two sets of primers for amplifying two or more nucleic acids, wherein the two or more nucleic acids comprise a polymorphism selected from any one of Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises sets of primers for amplifying nucleic acids comprising each of the polymorphisms provided in Table 4, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In another aspect, the present invention provides a genetic array comprising at least two sets of probes for hybridising to two or more nucleic acids, wherein the two or more nucleic acids comprise a polymorphism selected from any one of Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the array comprises probes hybridising to nucleic acids comprising each of the polymorphisms provided in Table 4, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an aspect, the present invention provides a computer implemented method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method operable in a computing system comprising a processor and a memory, the method comprising:

receiving genetic risk data for the human subject, wherein the genetic risk data was obtained by a method of the invention;

processing the data to obtain the risk of a human subject developing a severe response to a Coronavirus infection; and

outputting the risk of a human subject developing a severe response to a Coronavirus infection.

In an aspect, the present invention provides a computer implemented method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method operable in a computing system comprising a processor and a memory, the method comprising:

receiving clinical risk data and genetic risk data for the human subject, wherein the clinical risk data and genetic risk data were obtained by a method of the invention;

processing the data to combine the clinical risk data with the genetic risk data to obtain the risk of a human subject developing a severe response to a Coronavirus infection; and

outputting the risk of a human subject developing a severe response to a Coronavirus infection.

In a further aspect, the present invention provides a computer-implemented method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method operable in a computing system comprising a processor and a memory, the method comprising:

receiving at least one clinical variable associated with the human subject, wherein at least one clinical variable was obtained by a method of the invention;

processing the data to obtain the risk of a human subject developing a severe response to a Coronavirus infection; and

outputting the risk of a human subject developing a severe response to a Coronavirus infection.

In an embodiment of the three above aspects, processing the data is performed using a risk assessment model, where the risk assessment model has been trained using a training dataset comprising data relating to Coronavirus infection response severity and the genetic data and/or clinical data. In another embodiment, the method further comprises displaying or communicating the risk to a user.

In an aspect, the present invention provides a system for assessing the risk of a human subject developing a severe response to a Coronavirus infection comprising:

system instructions for performing a genetic risk assessment of the human subject according to a method of the invention; and

system instructions to obtain the risk of a human subject developing a severe response to a Coronavirus infection.

In an aspect, the present invention provides a system for assessing the risk of a human subject developing a severe response to a Coronavirus infection comprising:

system instructions for performing a clinical risk assessment and a genetic risk assessment of the human subject according to a method of the invention; and

system instructions for combining the clinical risk assessment and the genetic risk assessment to obtain the risk of a human subject developing a severe response to a Coronavirus infection.

In an aspect, the present invention provides a system for assessing the risk of a human subject developing a severe response to a Coronavirus infection comprising:

system instructions for performing a clinical risk assessment of the human subject using the method according to any one of claims 20 to 26 or 36 to 39; and

system instructions to obtain the risk of a human subject developing a severe response to a Coronavirus infection.

In an embodiment, the risk data for the subject is received from a user interface coupled to the computing system. In another embodiment, the risk data for the subject is received from a remote device across a wireless communications network. In another embodiment, the user interface or remote device is a SNP array platform. In another embodiment, outputting comprises outputting information to a user interface coupled to the computing system. In another embodiment, outputting comprises transmitting information to a remote device across a wireless communications network.

Any embodiment herein shall be taken to apply mutatis mutandis to any other embodiment unless specifically stated otherwise.

The present invention is not to be limited in scope by the specific embodiments described herein, which are intended for the purpose of exemplification only.

Functionally-equivalent products, compositions and methods are clearly within the scope of the invention, as described herein.

Throughout this specification, unless specifically stated otherwise or the context requires otherwise, reference to a single step, composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter.

The invention is hereinafter described by way of the following non-limiting Examples and with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

FIG. 1. Receiver operating characteristic curves for models with different amounts of information. The area under the receiver operating characteristic curve was 0.786 for the combined model, 0.723 for the clinical model, 0.680 for the SNP score, and 0.635 for the age and sex model.

FIG. 2. Distribution of COVID risk score for (a) cases and (b) controls. Note that 130 (13%) cases and 6 (1%) controls with scores over 15 have been omitted to facilitate the display of the distribution.

FIG. 3. Distribution of COVID-19 risk score in UK Biobank. Note that 7,769 (1.8%) scores over 15 have been omitted to facilitate the display of the distribution.

FIG. 4. Receiver operating characteristic curves for the age and sex model and the “full model” in the 30% validation dataset. The new model has an area under the curve (AUC) of 0.732 (95% CI=0.708, 0.756) and the age and sex model has an AUC of 0.671 (95% CI=0.646, 0.696).

FIG. 5. Calibration plots for the (A) age and sex model and (B) “full model” in the validation dataset.

FIG. 6. Distribution of probability of severe COVID-19 in all of UK Biobank for (A) age and sex model and (B) the full model.

DETAILED DESCRIPTION OF THE INVENTION General Techniques and Definitions

Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., epidemiological analysis, molecular genetics, risk assessment and clinical studies).

Unless otherwise indicated, the recombinant protein, cell culture, and immunological techniques utilized in the present invention are standard procedures, well known to those skilled in the art. Such techniques are described and explained throughout the literature in sources such as, J. Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons (1984), J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbour Laboratory Press (1989), T. A. Brown (editor), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press (1991), D. M. Glover and B. D. Hames (editors), DNA Cloning: A Practical Approach, Volumes 1-4, IRL Press (1995 and 1996), and F. M. Ausubel et al. (editors), Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-Interscience (1988, including all updates until present), Ed Harlow and David Lane (editors) Antibodies: A Laboratory Manual, Cold Spring Harbour Laboratory, (1988), and J. E. Coligan et al. (editors) Current Protocols in Immunology, John Wiley & Sons (including all updates until present).

It is to be understood that this disclosure is not limited to particular embodiments, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. As used in this specification and the appended claims, terms in the singular and the singular forms “a,” “an” and “the,” for example, optionally include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to “a probe” optionally includes a plurality of probe molecules; similarly, depending on the context, use of the term “a nucleic acid” optionally includes, as a practical matter, many copies of that nucleic acid molecule.

The term “and/or”, e.g., “X and/or Y” shall be understood to mean either “X and Y” or “X or Y” and shall be taken to provide explicit support for both meanings or for either meaning.

As used herein, the term “about”, unless stated to the contrary, refers to +/−10%, more preferably +/−5%, more preferably +/−1%, of the designated value.

Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

“Coronavirus” is a group of related RNA viruses that typically cause diseases in mammals and birds, such as respiratory tract infections in humans. Coronaviruses constitute the subfamily Orthocoronavirinae in the family Coronaviridae. Coronaviruses are enveloped viruses with a positive-sense single-stranded RNA genome and a nucleocapsid of helical symmetry. Coronaviruses have characteristic club-shaped spikes that project from their surface. Examples of Coronaviruses which cause disease in humans include, but are not necessarily limited to, Severe acute respiratory syndrome-related coronavirus (SARS-CoV or SARS-CoV-2), Middle East respiratory syndrome-related coronavirus (MERS), Human coronavirus OC43, Human coronavirus HKU1, Human coronavirus 229E and Human coronavirus NL63. In some embodiments, the SARS-CoV-2 the strain is selected from, but not limited to, the L strain, the S strain, the V strain, the G strain, the GR strain, the GH strain, hCoV-19/Australia/VIC01/2020, BetaCoV/Wuhan/WIV04/2019, B.1.1.7 variant, B.1.351 variant, B.1.427 variant, B.1.429 variant and P.1 variant.

As used herein, “risk assessment” refers to a process by which a subject's risk of developing a severe response to a Coronavirus infection can be assessed. A risk assessment will typically involve obtaining information relevant to the subject's risk of developing a severe response to a Coronavirus infection, assessing that information, and quantifying the subject's risk of developing a severe response to a Coronavirus infection, for example, by producing a risk score.

As used herein, the term “a severe response to a Coronavirus infection” encompasses any factor, or a symptom thereof, considered by a medical practitioner that would warrant the subject being hospitalised, the subject's life being at risk, or the subject requiring assistance to breath. Examples of symptoms of a severe response to a Coronavirus infection include, but are not limited to, difficulty breathing or shortness of breath, chest pain or pressure, loss of speech or loss of movement. A phenotype that displays a predisposition for a severe response to a Coronavirus infection, can, for example, show a higher likelihood that a severe response to a Coronavirus infection will develop in an individual with the phenotype than in members of a relevant general population under a given set of environmental conditions (diet, physical activity regime, geographic location, etc.).

As used herein, “biological sample” refers to any sample comprising nucleic acids, especially DNA, from or derived from a human patient, e.g., bodily fluids (blood, saliva, urine etc.), biopsy, tissue, and/or waste from the patient. Thus, tissue biopsies, stool, sputum, saliva, blood, lymph, or the like can easily be screened for polymorphisms, as can essentially any tissue of interest that contains the appropriate nucleic acids. In one embodiment, the biological sample is a cheek cell sample. These samples are typically taken, following informed consent, from a patient by standard medical laboratory methods. The sample may be in a form taken directly from the patient, or may be at least partially processed (purified) to remove at least some non-nucleic acid material.

As used herein, “gender” and “sex” are used interchangeably and refer to an individual's biological reproductive anatomy. In an embodiment, an individual's gender/sex is self-identified.

As used herein, “human subject”, “human” and subject” are used interchangeably and refer to the individual being assessed for risk of developing a severe response to a coronavirus infection.

A “polymorphism” is a locus that is variable; that is, within a population, the nucleotide sequence at a polymorphism has more than one version or allele. One example of a polymorphism is a “single nucleotide polymorphism” (SNP), which is a polymorphism at a single nucleotide position in a genome (the nucleotide at the specified position varies between individuals or populations). Other examples include a deletion or insertion of one or more base pairs at the polymorphism locus.

As used herein, the term “SNP” or “single nucleotide polymorphism” refers to a genetic variation between individuals; e.g., a single nitrogenous base position in the DNA of organisms that is variable. As used herein, “SNPs” is the plural of SNP. Of course, when one refers to DNA herein, such reference may include derivatives of the DNA such as amplicons, RNA transcripts thereof, etc.

The term “allele” refers to one of two or more different nucleotide sequences that occur or are encoded at a specific locus, or two or more different polypeptide sequences encoded by such a locus. For example, a first allele can occur on one chromosome, while a second allele occurs on a second homologous chromosome, e.g., as occurs for different chromosomes of a heterozygous individual, or between different homozygous or heterozygous individuals in a population. An allele “positively” correlates with a trait when it is linked to it and when presence of the allele is an indicator that the trait or trait form will occur in an individual comprising the allele. An allele “negatively” correlates with a trait when it is linked to it and when presence of the allele is an indicator that a trait or trait form will not occur in an individual comprising the allele.

A marker polymorphism or allele is “correlated” or “associated” with a specified phenotype (a severe response to a Coronavirus infection susceptibility, etc.) when it can be statistically linked (positively or negatively) to the phenotype. Methods for determining whether a polymorphism or allele is statistically linked are known to those in the art. That is, the specified polymorphism occurs more commonly in a case population (e.g., a severe response to a Coronavirus infection patients) than in a control population (e.g., individuals that do not have a severe response to a Coronavirus infection). This correlation is often inferred as being causal in nature, but it need not be, simple genetic linkage to (association with) a locus for a trait that underlies the phenotype is sufficient for correlation/association to occur.

The phrase “linkage disequilibrium” (LD) is used to describe the statistical correlation between two neighbouring polymorphic genotypes. Typically, LD refers to the correlation between the alleles of a random gamete at the two loci, assuming Hardy-Weinberg equilibrium (statistical independence) between gametes. LD is quantified with either Lewontin's parameter of association (D′) or with Pearson correlation coefficient (r) (Devlin and Risch, 1995). Two loci with a LD value of 1 are said to be in complete LD. At the other extreme, two loci with a LD value of 0 are termed to be in linkage equilibrium. Linkage disequilibrium is calculated following the application of the expectation maximization algorithm (EM) for the estimation of haplotype frequencies (Slatkin and Excoffier, 1996). LD (r²) values according to the present disclosure for neighbouring genotypes/loci are selected above 0.1, preferably, above 0.2, more preferable above 0.5, more preferably, above 0.6, still more preferably, above 0.7, preferably, above 0.8, more preferably above 0.9, ideally about 1.0.

Another way one of skill in the art can readily identify polymorphisms in linkage disequilibrium with the polymorphisms of the present disclosure is determining the LOD score for two loci. LOD stands for “logarithm of the odds”, a statistical estimate of whether two genes, or a gene and a disease gene, are likely to be located near each other on a chromosome and are therefore likely to be inherited. A LOD score of between about 2-3 or higher is generally understood to mean that two genes are located close to each other on the chromosome. Various examples of polymorphisms in linkage disequilibrium with the polymorphisms of the present disclosure are shown in Tables 1 to 6, 8, 19 or 22. The present inventors have found that many of the polymorphisms in linkage disequilibrium with the polymorphisms of the present disclosure have a LOD score of between about 2-50. Accordingly, in an embodiment, LOD values according to the present disclosure for neighbouring genotypes/loci are selected at least above 2, at least above 3, at least above 4, at least above 5, at least above 6, at least above 7, at least above 8, at least above 9, at least above 10, at least above 20 at least above 30, at least above 40, at least above 50.

In another embodiment, polymorphisms in linkage disequilibrium with the polymorphisms of the present disclosure can have a specified genetic recombination distance of less than or equal to about 20 centimorgan (cM) or less. For example, 15 cM or less, 10 cM or less, 9 cM or less, 8 cM or less, 7 cM or less, 6 cM or less, 5 cM or less, 4 cM or less, 3 cM or less, 2 cM or less, 1 cM or less, 0.75 cM or less, 0.5 cM or less, 0.25 cM or less, or 0.1 cM or less. For example, two linked loci within a single chromosome segment can undergo recombination during meiosis with each other at a frequency of less than or equal to about 20%, about 19%, about 18%, about 17%, about 16%, about 15%, about 14%, about 13%, about 12%, about 11%, about 10%, about 9%, about 8%, about 7%, about 6%, about 5%, about 4%, about 3%, about 2%, about 1%, about 0.75%, about 0.5%, about 0.25%, or about 0.1% or less.

In another embodiment, polymorphisms in linkage disequilibrium with the polymorphisms of the present disclosure are within at least 100 kb (which correlates in humans to about 0.1 cM, depending on local recombination rate), at least 50 kb, at least kb or less of each other.

For example, one approach for the identification of surrogate markers for a particular polymorphism involves a simple strategy that presumes that polymorphisms surrounding the target polymorphism are in linkage disequilibrium and can therefore provide information about disease susceptibility. Thus, as described herein, surrogate markers can therefore be identified from publicly available databases, such as HAPMAP, by searching for polymorphisms fulfilling certain criteria which have been found in the scientific community to be suitable for the selection of surrogate marker candidates (see, for example, Table 6a which provides surrogates of the polymorphisms in Table 3, and Table 6b which provides surrogates of the polymorphisms in Table 4).

“Allele frequency” refers to the frequency (proportion or percentage) at which an allele is present at a locus within an individual, within a line or within a population of lines. For example, for an allele “A,” diploid individuals of genotype “AA,” “Aa,” or “aa” have allele frequencies of 1.0, 0.5, or 0.0, respectively. One can estimate the allele frequency within a line or population (e.g., cases or controls) by averaging the allele frequencies of a sample of individuals from that line or population. Similarly, one can calculate the allele frequency within a population of lines by averaging the allele frequencies of lines that make up the population. In an embodiment, the term “allele frequency” is used to define the minor allele frequency (MAF). MAF refers to the frequency at which the least common allele occurs in a given population.

An individual is “homozygous” if the individual has only one type of allele at a given locus (e.g., a diploid individual has a copy of the same allele at a locus for each of two homologous chromosomes). An individual is “heterozygous” if more than one allele type is present at a given locus (e.g., a diploid individual with one copy each of two different alleles). The term “homogeneity” indicates that members of a group have the same genotype at one or more specific loci. In contrast, the term “heterogeneity” is used to indicate that individuals within the group differ in genotype at one or more specific loci.

A “locus” is a chromosomal position or region. For example, a polymorphic locus is a position or region where a polymorphic nucleic acid, trait determinant, gene or marker is located. In a further example, a “gene locus” is a specific chromosome location (region) in the genome of a species where a specific gene can be found.

A “marker,” “molecular marker” or “marker nucleic acid” refers to a nucleotide sequence or encoded product thereof (e.g., a protein) used as a point of reference when identifying a locus or a linked locus. A marker can be derived from genomic nucleotide sequence or from expressed nucleotide sequences (e.g., from an RNA, nRNA, mRNA, a cDNA, etc.), or from an encoded polypeptide. The term also refers to nucleic acid sequences complementary to or flanking the marker sequences, such as nucleic acids used as probes or primer pairs capable of amplifying the marker sequence. A “marker probe” is a nucleic acid sequence or molecule that can be used to identify the presence of a marker locus, e.g., a nucleic acid probe that is complementary to a marker locus sequence. Nucleic acids are “complementary” when they specifically hybridize in solution, e.g., according to Watson-Crick base pairing rules. A “marker locus” is a locus that can be used to track the presence of a second linked locus, e.g., a linked or correlated locus that encodes or contributes to the population variation of a phenotypic trait. For example, a marker locus can be used to monitor segregation of alleles at a locus, such as a quantitative trait locus (QTL), that are genetically or physically linked to the marker locus. Thus, a “marker allele,” alternatively an “allele of a marker locus” is one of a plurality of polymorphic nucleotide sequences found at a marker locus in a population that is polymorphic for the marker locus. Each of the identified markers is expected to be in close physical and genetic proximity (resulting in physical and/or genetic linkage) to a genetic element, e.g., a QTL, that contributes to the relevant phenotype. Markers corresponding to genetic polymorphisms between members of a population can be detected by methods well-established in the art. These include, e.g., DNA sequencing, PCR-based sequence specific amplification methods, detection of restriction fragment length polymorphisms (RFLP), detection of isozyme markers, detection of allele specific hybridization (ASH), detection of single nucleotide extension, detection of amplified variable sequences of the genome, detection of self-sustained sequence replication, detection of simple sequence repeats (SSRs), detection of single nucleotide polymorphisms (SNPs), or detection of amplified fragment length polymorphisms (AFLPs).

The term “amplifying” in the context of nucleic acid amplification is any process whereby additional copies of a selected nucleic acid (or a transcribed form thereof) are produced. Typical amplification methods include various polymerase based replication methods, including the polymerase chain reaction (PCR), ligase mediated methods such as the ligase chain reaction (LCR) and RNA polymerase based amplification (e.g., by transcription) methods.

An “amplicon” is an amplified nucleic acid, e.g., a nucleic acid that is produced by amplifying a template nucleic acid by any available amplification method (e.g., PCR, LCR, transcription, or the like).

A “gene” is one or more sequence(s) of nucleotides in a genome that together encode one or more expressed molecules, e.g., an RNA, or polypeptide. The gene can include coding sequences that are transcribed into RNA which may then be translated into a polypeptide sequence, and can include associated structural or regulatory sequences that aid in replication or expression of the gene.

A “genotype” is the genetic constitution of an individual (or group of individuals) at one or more genetic loci. Genotype is defined by the allele(s) of one or more known loci of the individual, typically, the compilation of alleles inherited from its parents.

A “haplotype” is the genotype of an individual at a plurality of genetic loci on a single DNA strand. Typically, the genetic loci described by a haplotype are physically and genetically linked, i.e., on the same chromosome strand.

A “set” of markers (polymorphisms), probes or primers refers to a collection or group of markers probes, primers, or the data derived therefrom, used for a common purpose, e.g., identifying an individual with a specified genotype (e.g., risk of developing a severe response to a Coronavirus infection). Frequently, data corresponding to the markers, probes or primers, or derived from their use, is stored in an electronic medium. While each of the members of a set possess utility with respect to the specified purpose, individual markers selected from the set as well as subsets including some, but not all of the markers, are also effective in achieving the specified purpose.

The polymorphisms and genes, and corresponding marker probes, amplicons or primers described above can be embodied in any system herein, either in the form of physical nucleic acids, or in the form of system instructions that include sequence information for the nucleic acids. For example, the system can include primers or amplicons corresponding to (or that amplify a portion of) a gene or polymorphism described herein. As in the methods above, the set of marker probes or primers optionally detects a plurality of polymorphisms in a plurality of said genes or genetic loci. Thus, for example, the set of marker probes or primers detects at least one polymorphism in each of these polymorphisms or genes, or any other polymorphism, gene or locus defined herein. Any such probe or primer can include a nucleotide sequence of any such polymorphism or gene, or a complementary nucleic acid thereof, or a transcribed product thereof (e.g., a nRNA or mRNA form produced from a genomic sequence, e.g., by transcription or splicing).

As used herein, “Receiver operating characteristic curves” (ROC) refer to a graphical plot of the sensitivity vs. (1—specificity) for a binary classifier system as its discrimination threshold is varied. The ROC can also be represented equivalently by plotting the fraction of true positives (TPR=true positive rate) vs. the fraction of false positives (FPR=false positive rate). Also known as a Relative Operating Characteristic curve, because it is a comparison of two operating characteristics (TPR & FPR) as the criterion changes. ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. Methods of using in the context of the disclosure will be clear to those skilled in the art.

As used herein, the phrase “combining the first clinical risk assessment and the genetic risk assessment” refers to any suitable mathematical analysis relying on the results of the assessments. For example, the results of the first clinical risk assessment and the genetic risk assessment may be added, more preferably multiplied.

As used herein, the terms “routinely screening for a severe response to a Coronavirus infection” and “more frequent screening” are relative terms, and are based on a comparison to the level of screening recommended to a subject who has no identified risk of developing a severe response to a Coronavirus infection.

Genetic Risk Assessment

In an aspect, a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection of the invention involves detecting the presence of a polymorphism provided in any one of Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium therewith. In another aspect, a method of the invention involves a genetic risk assessment performed by analysing the genotype of the subject at two or more loci for polymorphisms associated with a severe response to a Coronavirus infection. Various exemplary polymorphisms associated with a severe response to a Coronavirus infection are discussed in the present disclosure. These polymorphisms vary in terms of penetrance and many would be understood by those of skill in the art to be low penetrance polymorphisms.

The term “penetrance” is used in the context of the present disclosure to refer to the frequency at which a particular polymorphism manifests itself within human subjects with a severe response to a Coronavirus infection. “High penetrance” polymorphisms will almost always be apparent in a human subject with a severe response to a Coronavirus infection while “low penetrance” polymorphisms will only sometimes be apparent. In an embodiment polymorphisms assessed as part of a genetic risk assessment according to the present disclosure are low penetrance polymorphisms. As the skilled addressee will appreciate, each polymorphism which increases the risk of developing a severe response to a Coronavirus infection has an odds ratio of association with a severe response to a Coronavirus infection of greater than 1.0. In an embodiment, the odds ratio is greater than 1.02. Each polymorphism which decreases the risk of developing a severe response to a Coronavirus infection has an odds ratio of association with a severe response to a Coronavirus infection of less than 1.0. In an embodiment, the odds ratio is less than 0.98. Examples of such polymorphisms include, but are not limited to, those provided in Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium with one or more thereof. In an embodiment, the genetic risk assessment involves assessing polymorphisms associated with increased risk of developing a severe response to a Coronavirus infection. In another embodiment, the genetic risk assessment involves assessing polymorphisms associated with decreased risk of developing a severe response to a Coronavirus infection. In another embodiment, the genetic risk assessment involves assessing polymorphisms associated with an increased risk of developing a severe response to a Coronavirus infection and polymorphisms associated with a decreased risk of developing a severe response to a Coronavirus infection.

In an embodiment, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 polymorphisms associated with a severe response to a Coronavirus infection are analysed.

In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 polymorphisms associated with a severe response to a Coronavirus infection are selected from the polymorphisms provided in Tables 1 to 3, 5a or 6, Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium with one or more thereof

TABLE 1 Informative polymorphisms of the invention. p-value Chro- for mo- associ- some Position SNP ID Alleles ation 1 31624029 rs12083278 G, C 0.00000243 1 87628173 rs10873821 C, T 0.0000228 1 63766718 rs112728381 C, T 0.0000252 1 2998313 rs12745140 G, A 0.0000317 1 36374101 rs2765013 C, T 0.000039 1 36549664 rs2274122 G, A 0.000107 1 186287454 rs1830344 T, C 0.000127 1 187364290 rs7517532 G, C 0.000136 1 114893146 rs574339 T, C 0.000169 1 222722631 rs61825527 A, C 0.000194 1 31380174 rs4303117 A, C 0.000199 1 88237749 rs72714531 T, C 0.000223 1 101661978 rs11166552 C, T 0.000257 1 37147203 rs219007 C, T 0.000271 1 184803508 rs630030 A, G 0.000271 1 60210649 rs1004772 T, C 0.000289 1 83665753 rs9432945 C, T 0.000293 1 36172029 rs6664663 C, G 0.000304 1 161229986 rs5778200 A, AC 0.000326 1 107941708 rs17018870 G, A 0.000328 1 14216150 rs17350970 C, T 0.000346 1 55046392 rs300269 G, A 0.000355 1 230906775 rs3790971 A, G 0.000357 1 60214250 rs3990361 A, G 0.000386 1 208388679 rs78771609 A, G 0.000395 1 3680362 rs146866117 T, C 3.21516E−05 1 10993680 rs75721992 C, T 8.03153E−09 1 15698556 rs12562412 G, C 7.34872E−05 1 15758944 rs117338853 A, G 9.31685E−05 1 16109212 rs72647169 G, C 1.72286E−05 1 17295659 rs199765517 T, C 6.47434E−05 1 20072025 rs199727655 A, G 3.41489E−05 1 22538788 rs78360109 A, G 1.32276E−06 1 34829829 rs79955780 G, A 8.74145E−05 1 38661814 rs61778695 C, A 4.43093E−05 1 67804320 rs578200723 GTTA, G 0.000020482 1 72488455 rs116544454 T, G 1.85829E−05 1 109823418 rs144022094 T, C 3.57493E−05 1 109933450 rs56072034 A, G 4.12342E−05 1 168696733 rs76129265 T, G 7.51539E−05 1 204092087 rs201772428 A, G 4.3011E−06 1 206776460 rs35252702 A, G 1.56671E−07 1 207610967 rs61821114 T, C 3.00757E−05 1 210901242 rs1934624 A, T 5.48665E−05 1 218938774 rs76354174 A, G 7.54499E−05 1 230907829 rs143186556 A, G 5.46536E−05 1 231133014 rs200114138 A, G 3.96515E−06 1 46618634 rs17102023 G, A 0.5 1 150271556 rs115492982 A, G 0.01 1 152684866 rs2224986 T, C 0.8 1 192526317 rs74508649 T, C 0.9 1 239197542 rs112317747 C, T 0.05 2 36905013 rs6714112 C, A 0.00000781 2 217524986 rs2270360 A, C 0.0000245 2 11239618 rs62120103 C, T 0.000116 2 42181679 rs6740960 A, T 0.00012 2 137073048 rs6430625 A, G 0.000144 2 75788396 rs759255 C, A 0.000181 2 11694251 rs4313952 A, G 0.000194 2 140976470 rs9941558 A, G 0.000196 2 13900135 rs61101702 TAATA, T 0.0002 2 182396974 rs6760007 T, C 0.0002 2 46584059 rs34136947 T, G 0.000222 2 6948980 rs55900661 G, A 0.000235 2 175367762 rs4972443 T, C 0.000237 2 217553774 rs3755137 T, A 0.000263 2 2965401 rs1729903 C, T 0.000271 2 182359592 rs16867434 T, C 0.000275 2 50825372 rs116302817 C, T 0.000322 2 217701606 rs111437052 A, G 0.000343 2 52551249 rs115352379 T, C 0.000344 2 45503541 rs6749256 G, C 0.000397 2 11662023 rs62120186 C, T 0.000403 2 52998723 rs62127009 A, G 0.000408 2 22958939 rs59447738 G, T 8.21388E−05 2 23005876 rs73918088 A, C 7.50922E−05 2 25861939 rs189303418 T, C 0.000040202 2 34108876 rs1718746 C, G 8.52475E−05 2 34119632 rs1705143 A, G 8.08594E−05 2 64630299 rs872241 G, A 7.17367E−05 2 64641736 rs11676644 C, T 0.000032141 2 115258481 rs56735442 A, G 3.59163E−05 2 122952399 rs75945051 G, A 4.38486E−05 2 126726522 rs76187206 C, G 5.74668E−05 2 160721407 chr2 A, AC 4.45501E−05 160721407 2 179428061 rs11896637 T, C 1.81846E−06 2 179441917 rs72646881 C, T 1.59099E−05 2 179612315 rs145581345 C, T 8.45307E−05 2 181910717 rs78593095 A, G 2.26398E−05 2 191278341 rs6725814 G, A 1.88302E−05 2 79895332 rs183569214 G, T 0.7 2 80029580 rs77764981 T, G 0.6 2 182353446 rs2034831 A, G 0.02 3 141408691 rs6440031 A, G 0.0000714 3 1093795 rs1504061 C, G 0.0000716 3 125837737 rs1868132 C, T 0.000102 3 125649876 rs6438947 T, C 0.000126 3 38759465 rs9990137 A, G 0.000148 3 155736888 rs4680228 C, T 0.000157 3 24759067 rs71328493 C, G 0.000213 3 46628726 rs1829538 G, T 0.000257 3 71238088 rs111323182 T, A 0.00027 3 143909347 rs13089585 T, C 0.000302 3 2357581 rs56035150 A, G 0.000318 3 5893097 rs74827709 G, T 0.000342 3 21751839 rs1080021 A, G 0.000366 3 25519583 rs1864903 A, G 0.000366 3 173319456 rs6800283 T, C 0.000366 3 195949310 rs35516030 AG, A 0.000375 3 170214459 rs2008829 G, T 0.000376 3 38736230 rs12639182 C, T 0.000395 3 18486173 rs62240975 G, A 0.000405 3 170266664 rs139670481 AAATT, A 0.000406 3 170961913 rs115037737 G, A 0.000407 3 2748191 rs80225140 G, A 8.74289E−06 3 34944013 rs17032477 G, A 3.63638E−05 3 65100060 rs2128405 C, A 1.71219E−05 3 70783491 rs6766000 T, C 2.00031E−05 3 81810551 rs35196441 T, G 3.53506E−05 3 154812638 rs2196521 G, A 3.73634E−05 3 160379672 rs4679910 G, A 1.90222E−05 3 171853417 rs73167212 G, A 0.00002702 3 27188298 rs17317135 A, G 0.0000641 3 177796194 rs74911757 T, C 1.43291E−05 3 3184653 rs1705826 G, C 0.5 3 45841938 rs35896106 T, C 0.04 3 45900634 rs76374459 C, G 0.03 3 45908514 rs35652899 G, C 0.04 3 45916222 rs12639224 T, C 0.7 3 45916786 rs34901975 A, G 0.09 3 46018781 rs71615437 G, A 0.1 3 46049765 rs13433997 C, T 0.1 3 46180416 rs10510749 T, C 0.9 3 46222037 rs115102354 G, A 0.7 3 62936766 rs13062942 G, A 0.09 3 148718087 rs76488148 T, G 0.03 4 69705994 rs115162070 G, A 0.00000356 4 44418592 rs35540967 T, C 0.0000289 4 5821922 rs3774882 C, G 0.0000349 4 5821877 rs3774881 T, C 0.0000459 4 112613026 rs112641600 C, T 0.000058 4 106943200 rs11729561 T, C 0.000104 4 163076494 rs62331317 G, A 0.000113 4 5820342 rs28426993 C, T 0.000114 4 35945837 rs111866232 G, C 0.000157 4 106909473 rs78699658 A, G 0.000213 4 99439788 rs7668981 C, T 0.000273 4 187239747 rs7687352 A, G 0.000288 4 60236874 rs12498396 G, A 0.000292 4 60147523 rs13126577 T, C 0.000293 4 24316054 rs28735003 T, C 0.000295 4 96348686 rs4277782 T, C 0.000314 4 162009814 rs35850287 C, T 0.00033 4 39943691 rs115509062 G, C 6.54964E−05 4 45117625 rs75072424 A, G 2.80438E−05 4 69795670 rs144454074 A, G 4.19779E−05 4 73555556 rs28616128 G, A 3.02015E−06 4 75191300 rs115044024 C, T 4.15557E−05 4 84216649 rs144185023 A, G 3.72469E−05 4 87939942 rs76456240 C, T 9.48747E−05 4 121958473 rs202221151 C, T 1.61977E−06 4 142326546 rs76589765 G, A 4.20983E−06 4 151724769 rs116015734 T, C 9.26679E−05 4 153821201 rs62319956 C, A 2.01266E−06 4 170238293 rs76519323 A, C 2.30315E−05 4 170498270 rs74557505 C, T 6.57926E−05 4 181162752 rs17069033 T, G 1.35337E−05 4 185567903 rs4647611 C, G 2.72675E−05 4 27383278 rs6810404 A, C 0.0000988 5 173989338 rs2220543 T, A 0.0000187 5 180216905 rs10577599 CAT, C 0.000025 5 142252549 rs10039856 C, T 0.0000585 5 123950404 rs4240376 G, T 0.000065 5 122832716 rs62377777 T, C 0.0000653 5 59077872 rs62370540 T, C 0.000116 5 122559297 rs72787582 C, T 0.000117 5 124143238 rs71594388 C, T 0.000136 5 159258092 rs78045322 G, C 0.000172 5 135351183 rs72794907 G, A 0.000174 5 135435801 rs7720483 C, T 0.000174 5 122958050 rs70988587 ATTC, A 0.000215 5 176737309 rs7732626 A, G 0.000256 5 59191612 rs159616 C, T 0.000259 5 135360738 rs35901765 C, T 0.000275 5 106396918 rs6864971 C, T 0.000373 5 6471344 rs507971 C, T 0.00038 5 161475413 rs17548653 C, T 0.00041 5 6826973 rs275444 C, G 3.53004E−05 5 19993490 rs4466171 A, T 3.08251E−06 5 43280480 rs55770078 A, G 1.16965E−05 5 99815379 rs115319054 A, C 8.88589E−05 5 133519273 rs79601653 G, A 9.89897E−05 5 155405405 rs958444 T, C 2.01272E−05 5 160448591 rs11749317 C, T 0.000080983 5 180237828 rs113791144 T, C 0.000144 5 169590905 rs4478338 G, T 0.3 5 171480160 rs111265173 T, C 1 6 106326754 rs9386484 T, A 0.00000617 6 45704813 rs16873740 T, A 0.0000296 6 12216966 rs10755709 A, G 0.00003 6 18015447 rs140247774 C, T 0.000047 6 6925195 rs6933436 A, C 0.0000983 6 151433505 rs6928557 A, C 0.000121 6 170445103 rs9366129 C, T 0.000133 6 148557644 rs9390548 G, A 0.000149 6 137545805 rs11759276 A, T 0.000183 6 54128003 rs12193921 T, C 0.000184 6 54353221 rs76378690 G, A 0.000206 6 20013937 rs13213659 A, G 0.000233 6 148522455 rs117687499 G, A 0.000262 6 154813083 rs6935885 G, C 0.000306 6 18739469 rs2328283 A, C 0.000333 6 18271083 rs10949488 C, T 0.000355 6 20044587 rs594259 A, G 0.000375 6 44184719 rs2297393 T, C 0.000409 6 2885791 rs318470 C, A 9.76222E−05 6 16217762 rs149442766 A, G 3.42524E−05 6 26408145 rs144114619 A, T 4.57681E−05 6 33156845 rs41268014 G, C 1.61406E−05 6 36976747 rs140572234 C, G 6.20165E−08 6 36999980 rs114925152 G, C 0.000019845 6 37139030 rs35760989 C, G 2.15792E−05 6 101279459 rs9485415 T, C 2.95345E−05 6 147885588 rs140643252 A, G 0.000080315 6 27604726 rs61611950 T, C 0.8 7 114940068 rs7800941 G, A 0.000199 7 138401542 rs3778698 T, C 0.0002 7 99987236 rs7798226 T, G 0.000222 7 154700565 rs882469 G, A 0.000222 7 105125204 rs111283303 G, A 0.000232 7 11301277 rs7807069 G, A 0.000271 7 11362820 rs76731008 G, A 0.000277 7 9397414 rs13238693 T, G 0.000286 7 52994281 rs7804465 T, C 0.000342 7 36662714 rs58016731 AGTCTT, A 0.000391 7 6687563 rs55944391 C, A 0.000395 7 45244259 rs1294888 T, C 0.000395 7 14862658 rs2109498 A, T 0.000419 7 21730647 rs7790948 G, T 3.13489E−05 7 35720134 rs79496619 T, G 3.21712E−05 7 38677134 rs78966608 A, C 7.06676E−05 7 100483400 rs200675508 A, G 7.25535E−05 7 104782689 rs55743527 G, T 1.36022E−06 7 122122078 rs73431600 A, G 5.15467E−05 7 122196872 rs73433754 A, C 3.48011E−05 7 154926067 rs117164958 C, G 7.61301E−06 7 158928541 rs13225056 A, G 1.53596E−05 7 152960930 rs6967210 C, T 0.3 8 16790149 rs118072448 T, C 0.0000235 8 74268198 rs2010843 T, C 0.0000556 8 38897470 rs13282163 A, C 0.0000739 8 40181978 rs11779911 C, A 0.0000841 8 38821327 rs10808999 A, G 0.0000884 8 12850333 rs2947375 A, G 0.000162 8 143261211 rs72685583 T, C 0.000218 8 55488334 rs74458553 C, T 0.000254 8 32590598 rs4351382 T, A 0.000273 8 61303777 rs147353244 G, GA 0.000327 8 2044114 rs147776183 T, C 8.64871E−06 8 9478274 rs78876374 T, C 8.85355E−05 8 19218826 rs145746072 A, G 1.91735E−05 8 20354600 rs13249119 G, A 1.33633E−05 8 20447019 rs73626732 A, G 1.40229E−05 8 20460661 rs35258926 G, T 9.04111E−06 8 39799765 rs7845003 T, C 9.39614E−05 8 125839433 rs118040942 T, C 0.000059504 8 130677813 rs57557483 A, G 8.17573E−06 8 130694201 rs75572486 A, G 2.53314E−05 8 8730488 rs332040 A, G 0.9 9 4329170 rs3895472 T, C 0.0000235 9 21131627 rs12236000 G, C 0.0000452 9 75127404 rs35460846 A, AT 0.0000819 9 81158113 rs7027911 A, G 0.0000905 9 81158443 rs3009696 C, T 0.00013 9 140227646 rs11523787 C, T 0.000137 9 75061917 rs11143296 C, T 0.00014 9 78174461 rs13298924 T, C 0.00021 9 23650820 rs4584238 G, C 0.00022 9 35490636 rs10814241 C, G 0.00022 9 138202418 rs7032559 C, T 0.00025 9 122045518 rs487545 T, C 0.000269 9 139024232 rs7021573 G, A 0.000309 9 75136789 rs7022441 G, A 0.000343 9 139047766 rs10858230 G, A 0.000367 9 138006474 rs61018036 G, A 0.000373 9 116468053 rs75260470 C, T 0.000402 9 10276812 rs10959000 A, G 7.85152E−05 9 22080363 rs16905613 G, A 5.78633E−06 9 38669062 rs2993177 A, G 7.37838E−05 9 79453107 rs7853555 T, C 5.25711E−05 9 83032179 rs72744937 G, A 6.92799E−05 9 100137855 rs200908751 A, G 6.57652E−05 9 101874496 rs76094400 A, G 3.95481E−08 9 125704675 rs76670825 A, G 5.01185E−05 9 125903047 rs77089732 A, G 0.00005145 9 128794405 rs62570501 T, C 7.35517E−05 9 131771551 rs17455482 A, G 5.2118E−06 9 27121456 rs71480372 T, A 0.7 9 29688719 rs74790577 T, A 0.9 10 44015051 rs10793436 G, T 0.0000302 10 54100345 rs1441121 A, T 0.0000322 10 37454397 rs1892429 A, G 0.0000335 10 37277870 rs2091431 A, G 0.0000935 10 68576077 rs12218365 T, C 0.000113 10 54088932 rs75242872 C, G 0.000117 10 37370440 rs1794410 G, A 0.000118 10 90464714 rs303509 T, G 0.000118 10 43942080 rs12355127 G, A 0.000182 10 6079344 rs11256442 T, C 0.000183 10 6042472 rs17322780 A, G 0.000185 10 63303879 rs79189092 GA, G 0.000234 10 43563260 rs3026716 A, G 0.000246 10 109569850 rs12570947 T, C 0.000269 10 27204531 rs1815323 T, C 0.000276 10 6128547 rs7078273 G, T 0.000303 10 115225859 rs10430681 T, A 0.000303 10 63420128 rs11595927 C, T 0.000333 10 8109274 rs520236 C, G 0.000349 10 27929163 rs12774308 G, A 0.000354 10 31913890 rs11008551 G, T 0.000366 10 57401482 rs2463950 C, T 0.000366 10 72532238 rs2253801 T, C 0.000387 10 72223789 rs10740349 C, G 0.000394 10 3096047 rs12241312 C, A 0.000403 10 50140588 rs2940708 G, A 0.000404 10 67680203 rs41313840 G, A 7.19977E−08 10 127258691 rs7084502 A, G 7.81329E−05 10 9030308 rs71481792 A, T 0.0000223 10 131456440 rs79858702 T, C 0.000070971 10 123000638 rs5016035 G, T 0.9 11 2893867 rs10766439 A, G 0.0000937 11 2897875 rs4929952 C, T 0.00011 11 10531548 rs142012992 CTTAG, C 0.000111 11 69896252 rs4980753 A, G 0.000199 11 2902105 rs3864883 C, T 0.000222 11 126061372 rs35747384 G, T 0.000237 11 270987 rs7396066 C, T 0.000245 11 117755082 rs491292 C, T 0.000246 11 130323805 rs7109513 C, T 0.000256 11 120784855 rs2852238 G, C 0.000274 11 119248855 rs76560104 T, C 0.000279 11 1795214 rs79194907 G, A 6.83674E−05 11 5146083 rs12365149 T, C 3.11673E−05 11 44547746 rs12275504 G, T 1.40655E−05 11 46727333 rs149066130 A, G 7.98651E−05 11 57982229 rs1966836 A, G 6.87681E−05 11 65414949 rs199717374 T, C 1.05822E−05 11 78904266 rs75970706 T, C 0.000040305 11 94665002 rs76360689 A, G 1.05432E−06 11 133713033 rs75786498 A, G 3.13102E−05 12 8760610 rs11613792 A, G 0.00000256 12 106624953 rs12823094 T, A 0.0000633 12 25427178 rs140584644 C, T 0.000107 12 50375477 rs7979554 A, G 0.000158 12 76404693 rs1433362 T, G 0.000196 12 95127606 rs6538530 C, T 0.000207 12 77114964 rs12827237 A, G 0.000227 12 7606158 rs11611785 C, T 0.000234 12 126551205 rs11058488 C, T 0.000239 12 58267346 rs112976255 C, A 0.000259 12 129567952 rs58003804 A, G 0.000265 12 130254478 rs73160210 G, T 0.000267 12 68010614 rs1904551 T, C 0.000272 12 116784113 rs1732329 A, G 0.000287 12 25159263 rs859134 A, G 0.000368 12 129563020 rs2002553 G, A 0.000387 12 130242016 rs73436164 A, G 0.000389 12 125537439 rs145578156 AATTTTTT, A 0.000402 12 67821215 rs7955453 C, A 0.000403 12 41970164 rs970970 G, A 0.000405 12 2144357 rs75442877 T, C 0.000407 12 2174748 rs117821007 C, T 4.86991E−05 12 25681286 rs58907459 T, C 6.07118E−05 12 57589676 rs143285614 A, G 2.69071E−05 12 125302200 rs143093152 G, A 6.62377E−06 12 56084466 rs7397549 C, T 0.9 13 74558505 rs12871414 C, T 0.000094 13 23658838 rs1984162 A, G 0.000103 13 99778655 rs35784338 CT, C 0.000199 13 102622688 rs7339161 T, G 0.000203 13 101425653 rs9585503 T, G 0.00025 13 74804797 rs2039342 C, T 0.000345 13 98753461 rs545096 C, G 0.000349 13 71296869 rs2249209 A, G 0.000385 13 28230642 rs10712355 AT, A 0.000415 13 39433574 rs138539682 A, G 3.32149E−05 13 68302925 rs75022796 T, C 7.24669E−05 13 63178476 rs2649134 T, C 0.5 14 72908102 rs2238187 A, G 0.00000821 14 72934229 rs12587980 C, T 0.0000933 14 76011690 rs2734265 G, A 0.000178 14 72891494 rs2238191 C, A 0.000196 14 65225452 rs58725048 G, C 0.000269 14 41523312 rs11157189 A, G 0.000314 14 35857405 rs61988300 A, G 0.000315 14 97526363 rs75607541 T, A 0.000337 14 52276643 rs117852779 C, T 2.89233E−06 14 80405359 rs11159425 T, G 4.36025E−07 14 80570671 rs114463019 G, T 3.85403E−05 14 87813714 rs28450466 A, G 9.45754E−05 14 93016441 rs57851052 C, T 5.53318E−05 14 104863663 rs80083325 A, G 0.000073746 14 77692036 rs144114696 A, G 0.3 15 33908103 rs12593288 C, T 0.000023 15 33916053 rs2229117 G, C 0.0000561 15 27274425 rs149380649 CT, C 0.000112 15 81412674 rs2683240 C, T 0.000184 15 89165665 rs73451724 G, A 0.000235 15 33407307 rs17816808 A, G 0.000295 15 46666881 rs1994195 A, C 0.000295 15 33914240 rs16973353 A, T 0.000307 15 53279291 rs719715 G, A 0.000321 15 89162709 rs112248718 G, A 0.000406 15 22845849 rs150408740 G, A 1.76793E−05 15 34498314 rs75915717 T, C 1.0293E−06 15 41047777 rs35673728 C, T 6.67895E−05 15 41254865 rs12915860 C, A 1.01336E−05 15 41712936 rs62001419 A, C 9.84979E−05 15 52689631 rs1724577 T, G 2.42041E−05 15 58047086 rs77910305 G, C 1.30369E−05 15 65851028 rs200531541 A, G 3.83063E−06 15 78471034 rs34921279 T, C 2.40367E−05 15 84063245 rs12591031 G, A 1.16687E−05 15 91452594 rs142925505 T, C 6.04339E−07 15 45858905 rs77055952 G, A 0.5 15 48984345 rs74750712 G, T 0.4 16 78624025 rs72803978 A, G 0.0000612 16 4065412 rs12448453 A, G 0.000148 16 84006469 rs2250573 C, A 0.000148 16 6653119 rs12934582 T, C 0.000264 16 27040235 rs2063839 G, A 0.000272 16 6081670 rs8053942 C, T 0.000304 16 31404502 rs2454907 G, A 0.000304 16 85992829 rs11117428 T, C 0.000318 16 31392047 rs11574646 C, T 0.000358 16 78865144 rs68020681 T, G 0.000404 16 5898969 rs11647387 A, C 0.000406 16 49391921 rs62029091 A, G 7.11949E−05 16 49394276 rs8057939 C, T 1.12522E−05 16 60671279 rs118097562 T, A 1.90641E−05 16 61851413 rs151208133 T, C 6.3915E−06 16 81194912 rs11642802 C, A 4.32104E−06 16 90075827 rs201800670 T, C 1.60067E−05 16 10579876 rs72779789 C, G 0.9 16 49311043 rs145643452 A, G 0.9 17 9170408 rs34761447 C, T 0.0000262 17 29737612 rs178840 G, A 0.0000753 17 29740894 rs35054028 G, T 0.00025 17 18671675 rs55828488 G, A 0.000284 17 31676083 rs59341815 T, C 0.000294 17 3110572 rs34259120 C, A 0.00032 17 15548222 rs55821658 C, T 0.000365 17 1462712 rs73298816 G, A 5.44207E−05 17 3844344 rs144535413 T, C 1.11488E−05 17 36485146 rs147966258 A, G 1.64933E−05 17 39240563 rs193005959 A, C 3.72836E−05 17 55803083 rs72841559 C, G 2.26086E−05 17 56329775 rs368901060 ACCAT, A 4.70507E−06 17 63919929 rs7220318 G, A 2.39945E−05 17 72890474 rs689992 T, A 8.97907E−05 17 78215658 rs117140258 A, G 4.15567E−05 17 80443309 rs9890316 A, G 0.9 18 67208392 rs12958013 T, C 0.0000319 18 10016417 rs618909 C, A 0.000193 18 649311 rs9966612 A, G 0.00021 18 3899729 rs2667396 C, T 0.000259 18 59747387 rs652473 C, T 0.000338 18 9095227 rs16954792 T, C 0.000368 18 76506592 rs35409638 C, T 0.00039 18 67209524 rs34527658 A, AT 0.000391 18 4610215 rs76902871 G, T 2.95635E−05 18 13501162 rs2298530 C, T 6.48889E−05 18 14310187 rs117505121 G, A 3.59448E−05 18 49288587 rs117781678 A, C 4.32852E−05 18 76650871 rs7240086 G, A 6.95487E−06 18 30006171 rs142257532 C, T 1 19 44492164 rs60744406 A, G 0.000019 19 32023957 rs8105499 C, A 0.000103 19 3058098 rs3217064 C, CT 0.000188 19 50693096 rs648691 T, C 0.000229 19 6533402 rs3097296 T, C 0.000306 19 55500034 rs76616660 T, C 0.000312 19 15565046 rs9646651 G, A 0.000323 19 44436733 rs8100011 G, A 0.000334 19 57133633 rs35011777 C, T 0.000365 19 36018109 rs74726174 C, T 1.82372E−05 19 53333975 rs10411226 G, A 0.0000923 19 38867031 rs200403794 A, G 8.36021E−05 20 20344377 rs7270923 A, C 0.000159 20 53043792 rs6023232 G, T 0.000186 20 38792298 rs6016275 C, T 0.000192 20 45355986 rs2076293 A, G 0.000217 20 52985158 rs6097944 T, G 0.000284 20 8782776 rs138434221 A, C 4.80296E−06 20 15632993 rs6110707 C, T 1.31264E−05 20 55021575 rs6069749 T, C 2.92257E−05 20 55111747 rs6014757 A, G 6.44584E−05 20 39389409 rs56259900 G, A 0.6 20 60473717 rs76253189 G, C 1 21 43080428 rs2252109 A, T 0.0000428 21 43086264 rs2849697 T, C 0.000144 21 46991937 rs76902403 G, A 0.000155 21 41321695 rs11701006 G, A 0.000196 21 39963301 rs975846 A, G 0.000268 21 35423390 rs1986076 C, T 0.000297 21 39962001 rs9789875 C, T 0.000299 21 20402128 rs62216866 A, G 0.000367 21 19045795 rs73200561 A, T 9.40896E−05 21 37444937 rs2230191 A, G 3.4537E−07 21 44424444 rs75994231 T, C 0.7 22 22564734 rs5757427 A, T 0.00000237 22 49677464 rs62220604 G, A 0.0000355 22 24407483 rs11090305 T, C 0.0000377 22 44341300 rs17494724 G, A 0.000157 22 47986266 rs56813510 C, A 0.000189 22 44323597 rs139049 C, T 0.000306 22 28016883 rs1885362 A, C 4.65803E−05 22 40056937 rs113038998 T, C 1.37758E−05 22 44285118 rs117421847 A, G 6.60782E−05 22 22724951 rs7290963 T, G 0.0000716

TABLE 2 Informative polymorphisms of the invention - 306 polymorphism panel. Frequency Frequency Allele Allele Allele Allele p-value for Chromsome Position SNP ID 1 2 1 2 association OR 1 2998313 rs12745140 A G 0.088689 0.911311 0.000032 1.832076 1 14216150 rs17350970 T C 0.077425 0.922575 0.000346 0.7217155 1 31380174 rs4303117 A C 0.308602 0.691398 0.000199 0.725645 1 31624029 rs12083278 G C 0.295029 0.704971 0.000002 0.751250 1 36172029 rs6664663 G C 0.134612 0.865388 0.000304 0.816236 1 36374101 rs2765013 T C 0.086283 0.913717 0.000039 0.749941 1 36549664 rs2274122 G A 0.185863 0.814137 0.000107 0.810774 1 37147203 rs219007 T C 0.373057 0.626943 0.000271 1.161837 1 55046392 rs300269 G A 0.475448 0.524552 0.000355 1.118490 1 60210649 rs1004772 T C 0.156424 0.843576 0.000289 1.537451 1 60214250 rs3990361 G A 0.314799 0.685201 0.000386 0.880359 1 63766718 rs112728381 T C 0.310873 0.689127 0.000025 0.829006 1 83665753 rs9432945 T C 0.195772 0.804228 0.000293 1.244035 1 87628173 rs10873821 T C 0.247509 0.752491 0.000023 0.654805 1 88237749 rs72714531 C T 0.061964 0.938036 0.000223 0.615057 1 101661978 rs11166552 T C 0.338277 0.661723 0.000257 0.740338 1 107941708 rs17018870 A G 0.123982 0.876018 0.000328 1.316821 1 114893146 rs574339 C T 0.292471 0.707529 0.000169 0.838798 1 161229986 rs5778200 AC A 0.166927 0.833073 0.000326 1.100723 1 184803508 rs630030 A G 0.459053 0.540947 0.000271 0.776025 1 186287454 rs1830344 C T 0.099058 0.900942 0.000127 0.715555 1 187364290 rs7517532 C G 0.277242 0.722758 0.000136 1.274559 1 208388679 rs78771609 G A 0.057479 0.942521 0.000395 0.682444 1 222722631 rs61825527 C A 0.055134 0.944866 0.000194 0.684243 1 230906775 rs3790971 A G 0.296911 0.703089 0.000357 0.809538 2 2965401 rs1729903 T C 0.431488 0.568512 0.000271 1.363332 2 6948980 rs55900661 A G 0.069029 0.930971 0.000235 1.467029 2 11239618 rs62120103 T C 0.447468 0.552532 0.000116 0.728192 2 11662023 rs62120186 T C 0.141992 0.858008 0.000403 0.679629 2 11694251 rs4313952 G A 0.128053 0.871947 0.000194 0.692882 2 13900135 rs61101702 T TAATA 0.200010 0.799990 0.000200 0.686034 2 36905013 rs6714112 A C 0.138077 0.861923 0.000008 0.702041 2 42181679 rs6740960 A T 0.484269 0.515731 0.000120 0.838653 2 45503541 rs6749256 C G 0.125856 0.874144 0.000397 0.698683 2 46584059 rs34136947 G T 0.150999 0.849001 0.000222 0.764697 2 50825372 rs116302817 T C 0.055823 0.944177 0.000322 0.502987 2 52551249 rs115352379 C T 0.052932 0.947068 0.000344 0.802121 2 52998723 rs62127009 G A 0.146798 0.853202 0.000408 1.315507 2 75788396 rs759255 A C 0.179965 0.820035 0.000181 0.785823 2 137073048 rs6430625 G A 0.377976 0.622024 0.000144 0.874221 2 140976470 rs9941558 G A 0.215540 0.784460 0.000196 0.758804 2 175367762 rs4972443 C T 0.185607 0.814393 0.000237 0.755393 2 182359592 rs16867434 C T 0.086231 0.913769 0.000275 1.665760 2 182396974 rs6760007 C T 0.202409 0.797591 0.000200 1.303906 2 217524986 rs2270360 C A 0.265435 0.734565 0.000025 0.806294 2 217553774 rs3755137 A T 0.167060 0.832940 0.000263 0.869056 2 217701606 rs111437052 G A 0.045066 0.954934 0.000343 0.658309 3 1093795 rs1504061 G C 0.050105 0.949895 0.000072 1.878863 3 2357581 rs56035150 G A 0.051997 0.948003 0.000318 1.203568 3 5893097 rs74827709 T G 0.109834 0.890166 0.000342 0.744145 3 18486173 rs62240975 A G 0.238497 0.761503 0.000405 1.236323 3 21751839 rs1080021 G A 0.456598 0.543402 0.000366 1.189673 3 24759067 rs71328493 G C 0.121505 0.878495 0.000213 1.301830 3 25519583 rs1864903 G A 0.403176 0.596824 0.000366 1.057886 3 27188298 rs17317135 A G 0.048133 0.951867 0.000064 0.653752 3 38736230 rs12639182 T C 0.198184 0.801816 0.000395 0.806524 3 38759465 rs9990137 G A 0.341528 0.658472 0.000148 0.765458 3 46628726 rs1829538 G T 0.129451 0.870549 0.000257 0.757119 3 71238088 rs111323182 A T 0.044740 0.955260 0.000270 0.902081 3 125649876 rs6438947 C T 0.235079 0.764921 0.000126 1.208900 3 125837737 rs1868132 T C 0.111588 0.888412 0.000102 1.421540 3 141408691 rs6440031 A G 0.084557 0.915443 0.000071 0.730686 3 143909347 rs13089585 C T 0.050473 0.949527 0.000302 0.600404 3 155736888 rs4680228 T C 0.443082 0.556918 0.000157 1.248159 3 170214459 rs2008829 T G 0.285307 0.714693 0.000376 1.093430 3 170266664 rs139670481 A AAATT 0.141898 0.858102 0.000406 1.502990 3 170961913 rs115037737 A G 0.057828 0.942172 0.000407 0.607395 3 173319456 rs6800283 C T 0.089864 0.910136 0.000366 0.789994 3 195949310 rs35516030 A AG 0.263765 0.736235 0.000375 1.315177 4 5820342 rs28426993 T C 0.099661 0.900339 0.000114 0.575494 4 5821877 rs3774881 C T 0.151611 0.848389 0.000046 0.647570 4 5821922 rs3774882 G C 0.075890 0.924110 0.000035 0.573686 4 24316054 rs28735003 C T 0.144258 0.855742 0.000295 0.658173 4 27383278 rs6810404 A C 0.490985 0.509015 0.000099 0.776925 4 35945837 rs111866232 C G 0.039927 0.960073 0.000157 0.541922 4 44418592 rs35540967 C T 0.074931 0.925069 0.000029 1.777837 4 60147523 rs13126577 C T 0.497288 0.502712 0.000293 1.301281 4 60236874 rs12498396 A G 0.359748 0.640252 0.000292 0.875135 4 69705994 rs115162070 A G 0.069851 0.930149 0.000004 0.566347 4 96348686 rs4277782 T C 0.253317 0.746683 0.000314 1.099665 4 99439788 rs7668981 T C 0.434271 0.565729 0.000273 0.750526 4 106909473 rs78699658 G A 0.076853 0.923147 0.000213 0.769933 4 106943200 rs11729561 C T 0.080734 0.919266 0.000104 0.776444 4 112613026 rs112641600 T C 0.104676 0.895324 0.000058 0.646736 4 162009814 rs35850287 C T 0.469632 0.530368 0.000330 0.786370 4 163076494 rs62331317 A G 0.108942 0.891058 0.000113 1.821384 4 187239747 rs7687352 A G 0.486412 0.513588 0.000288 1.144337 5 6471344 rs507971 C T 0.210730 0.789270 0.000380 0.795710 5 59077872 rs62370540 C T 0.105859 0.894141 0.000116 0.634414 5 59191612 rs159616 C T 0.390352 0.609648 0.000259 0.881079 5 106396918 rs6864971 C T 0.379226 0.620774 0.000373 1.309747 5 122559297 rs72787582 T C 0.040661 0.959339 0.000117 0.495005 5 122832716 rs62377777 C T 0.219393 0.780607 0.000065 0.697966 5 122958050 rs70988587 A ATTC 0.192984 0.807016 0.000215 0.864676 5 123950404 rs4240376 T G 0.201643 0.798357 0.000065 0.798894 5 124143238 rs71594388 T C 0.074401 0.925599 0.000136 0.816311 5 135351183 rs72794907 A G 0.311611 0.688389 0.000174 0.907778 5 135360738 rs35901765 T C 0.455190 0.544810 0.000275 0.872551 5 135435801 rs7720483 T C 0.474933 0.525067 0.000174 0.778409 5 142252549 rs10039856 T C 0.097202 0.902798 0.000059 0.722279 5 159258092 rs78045322 C G 0.058813 0.941187 0.000172 0.665840 5 161475413 rs17548653 T C 0.051060 0.948940 0.000410 0.694777 5 173989338 rs2220543 A T 0.289838 0.710162 0.000019 0.753959 5 176737309 rs7732626 G A 0.075159 0.924841 0.000256 0.621750 5 180216905 rs10577599 C CAT 0.060491 0.939509 0.000025 1.117455 5 180237828 rs113791144 T C 0.066435 0.933565 0.000144 1.437219 6 6925195 rs6933436 C A 0.283852 0.716148 0.000098 1.259040 6 12216966 rs10755709 G A 0.300795 0.699205 0.000030 0.798524 6 18015447 rs140247774 T C 0.066938 0.933062 0.000047 1.795184 6 18271083 rs10949488 T C 0.058632 0.941368 0.000355 1.420327 6 18739469 rs2328283 C A 0.439080 0.560920 0.000333 1.340607 6 20013937 rs13213659 G A 0.356283 0.643717 0.000233 1.432682 6 20044587 rs594259 G A 0.405796 0.594204 0.000375 1.066926 6 44184719 rs2297393 C T 0.428032 0.571968 0.000409 0.802643 6 45704813 rs16873740 A T 0.118789 0.881211 0.000030 1.293166 6 54128003 rs12193921 C T 0.055659 0.944341 0.000184 1.567508 6 54353221 rs76378690 A G 0.133523 0.866477 0.000206 0.703482 6 106326754 rs9386484 A T 0.236292 0.763708 0.000006 0.700394 6 137545805 rs11759276 T A 0.066107 0.933893 0.000183 0.633909 6 148522455 rs117687499 A G 0.080216 0.919784 0.000262 0.638077 6 148557644 rs9390548 A G 0.172941 0.827059 0.000149 0.785698 6 151433505 rs6928557 A C 0.177199 0.822801 0.000121 0.662379 6 154813083 rs6935885 C G 0.146378 0.853622 0.000306 1.227099 6 170445103 rs9366129 T C 0.377194 0.622806 0.000133 1.169297 7 6687563 rs55944391 A C 0.287309 0.712691 0.000395 1.259924 7 9397414 rs13238693 T G 0.426972 0.573028 0.000286 1.212542 7 11301277 rs7807069 G A 0.485509 0.514491 0.000271 0.780193 7 11362820 rs76731008 A G 0.141480 0.858520 0.000277 1.500773 7 14862658 rs2109498 A T 0.153107 0.846893 0.000419 0.865912 7 36662714 rs58016731 A AGTCTT 0.095719 0.904281 0.000391 0.677730 7 45244259 rs1294888 T C 0.263570 0.736430 0.000395 0.684875 7 52994281 rs7804465 C T 0.374660 0.625340 0.000342 1.187107 7 99987236 rs7798226 G T 0.386582 0.613418 0.000222 1.238744 7 105125204 rs111283303 A G 0.157476 0.842524 0.000232 0.876698 7 114940068 rs7800941 A G 0.161413 0.838587 0.000199 1.305293 7 138401542 rs3778698 T C 0.254384 0.745616 0.000200 0.835206 7 154700565 rs882469 A G 0.238270 0.761730 0.000222 0.861757 8 12850333 rs2947375 A G 0.218314 0.781686 0.000162 0.682120 8 16790149 rs118072448 C T 0.076838 0.923162 0.000024 0.567696 8 32590598 rs4351382 A T 0.151498 0.848502 0.000273 1.643539 8 38821327 rs10808999 A G 0.133668 0.866332 0.000088 0.781512 8 38897470 rs13282163 C A 0.083088 0.916912 0.000074 0.697853 8 40181978 rs11779911 A C 0.334674 0.665326 0.000084 0.759358 8 55488334 rs74458553 T C 0.055479 0.944521 0.000254 0.734023 8 61303777 rs147353244 GA G 0.126562 0.873438 0.000327 0.753803 8 74268198 rs2010843 T C 0.468438 0.531562 0.000056 0.764239 8 143261211 rs72685583 C T 0.068522 0.931478 0.000218 1.571550 9 4329170 rs3895472 T C 0.073518 0.926482 0.000024 0.662754 9 21131627 rs12236000 C G 0.076624 0.923376 0.000045 0.668847 9 23650820 rs4584238 G C 0.319618 0.680382 0.000220 1.216505 9 35490636 rs10814241 G C 0.093586 0.906414 0.000220 1.562150 9 75061917 rs11143296 T C 0.451371 0.548629 0.000140 0.795898 9 75127404 rs35460846 A AT 0.481894 0.518106 0.000082 1.428832 9 75136789 rs7022441 A G 0.473267 0.526733 0.000343 0.709246 9 78174461 rs13298924 C T 0.237835 0.762165 0.000210 1.184631 9 81158113 rs7027911 A G 0.428022 0.571978 0.000091 1.184363 9 81158443 rs3009696 T C 0.260891 0.739109 0.000130 1.413299 9 116468053 rs75260470 T C 0.062434 0.937566 0.000402 0.657867 9 122045518 rs487545 C T 0.108712 0.891288 0.000269 0.816792 9 138006474 rs61018036 A G 0.178813 0.821187 0.000373 1.346902 9 138202418 rs7032559 T C 0.368961 0.631039 0.000250 1.228892 9 139024232 rs7021573 A G 0.438519 0.561481 0.000309 0.777563 9 139047766 rs10858230 A G 0.201935 0.798065 0.000367 1.208077 9 140227646 rs11523787 T C 0.469081 0.530919 0.000137 1.450898 10 3096047 rs12241312 C A 0.348689 0.651311 0.000403 0.798146 10 6042472 rs17322780 G A 0.096814 0.903186 0.000185 0.735578 10 6079344 rs11256442 T C 0.287927 0.712073 0.000183 0.767768 10 6128547 rs7078273 T G 0.405064 0.594936 0.000303 1.159191 10 8109274 rs520236 G C 0.219884 0.780116 0.000349 0.756563 10 9030308 rs71481792 A T 0.381127 0.618873 0.000022 1.192942 10 27204531 rs1815323 C T 0.111020 0.888980 0.000276 0.660612 10 27929163 rs12774308 A G 0.130100 0.869900 0.000354 0.718834 10 31913890 rs11008551 T G 0.195413 0.804587 0.000366 1.331756 10 37277870 rs2091431 A G 0.291382 0.708618 0.000094 0.746109 10 37370440 rs1794410 A G 0.411272 0.588728 0.000118 0.857482 10 37454397 rs1892429 G A 0.160418 0.839582 0.000034 0.745569 10 43563260 rs3026716 G A 0.253344 0.746656 0.000246 1.269510 10 43942080 rs12355127 A G 0.121879 0.878121 0.000182 1.223674 10 44015051 rs10793436 T G 0.317755 0.682245 0.000030 0.691022 10 50140588 rs2940708 G A 0.076009 0.923991 0.000404 0.667254 10 54088932 rs75242872 G C 0.064881 0.935119 0.000117 0.492357 10 54100345 rs1441121 A T 0.438240 0.561760 0.000032 0.821531 10 57401482 rs2463950 T C 0.076162 0.923838 0.000366 1.827848 10 63303879 rs79189092 G GA 0.170880 0.829120 0.000234 1.297045 10 63420128 rs11595927 T C 0.290509 0.709491 0.000333 1.236769 10 68576077 rs12218365 C T 0.138063 0.861937 0.000113 0.607017 10 72223789 rs10740349 C G 0.082859 0.917141 0.000394 1.272646 10 72532238 rs2253801 C T 0.423564 0.576436 0.000387 0.810933 10 90464714 rs303509 G T 0.275512 0.724488 0.000118 0.899337 10 109569850 rs12570947 C T 0.149219 0.850781 0.000269 1.403835 10 115225859 rs10430681 A T 0.074066 0.925934 0.000303 0.678792 11 270987 rs7396066 C T 0.187477 0.812523 0.000245 0.833428 11 2893867 rs10766439 A G 0.362551 0.637449 0.000094 1.423212 11 2897875 rs4929952 T C 0.202575 0.797425 0.000110 0.619703 11 2902105 rs3864883 T C 0.111433 0.888567 0.000222 0.576303 11 10531548 rs142012992 C CTTAG 0.325845 0.674155 0.000111 0.697824 11 69896252 rs4980753 A G 0.376448 0.623552 0.000199 1.158722 11 117755082 rs491292 T C 0.207093 0.792907 0.000246 1.437101 11 119248855 rs76560104 C T 0.102744 0.897256 0.000279 1.664471 11 120784855 rs2852238 G C 0.278141 0.721859 0.000274 1.343841 11 126061372 rs35747384 T G 0.105597 0.894403 0.000237 0.743710 11 130323805 rs7109513 T C 0.402800 0.597200 0.000256 0.770364 12 2144357 rs75442877 C T 0.065950 0.934050 0.000407 0.685377 12 7606158 rs11611785 C T 0.429591 0.570409 0.000234 1.060466 12 8760610 rs11613792 G A 0.138548 0.861452 0.000003 0.809562 12 25159263 rs859134 A G 0.496130 0.503870 0.000368 1.187565 12 25427178 rs140584644 T C 0.253790 0.746210 0.000107 1.424311 12 41970164 rs970970 G A 0.384510 0.615490 0.000405 1.165447 12 50375477 rs7979554 A G 0.280549 0.719451 0.000158 1.204227 12 58267346 rs112976255 A C 0.072297 0.927703 0.000259 0.688523 12 67821215 rs7955453 A C 0.099287 0.900713 0.000403 1.272926 12 68010614 rs1904551 C T 0.412363 0.587637 0.000272 0.880242 12 76404693 rs1433362 G T 0.097993 0.902007 0.000196 0.868310 12 77114964 rs12827237 G A 0.163516 0.836484 0.000227 0.712866 12 95127606 rs6538530 T C 0.208557 0.791443 0.000207 1.316565 12 106624953 rs12823094 A T 0.244075 0.755925 0.000063 1.166247 12 116784113 rs1732329 A G 0.350353 0.649647 0.000287 1.418587 12 125537439 rs145578156 A AATTTTTT 0.126772 0.873228 0.000402 1.346757 12 126551205 rs11058488 T C 0.042023 0.957977 0.000239 1.581584 12 129563020 rs2002553 A G 0.122964 0.877036 0.000387 0.901839 12 129567952 rs58003804 G A 0.091258 0.908742 0.000265 0.798624 12 130242016 rs73436164 G A 0.168624 0.831376 0.000389 0.655603 12 130254478 rs73160210 T G 0.081785 0.918215 0.000267 1.744820 13 23658838 rs1984162 G A 0.258794 0.741206 0.000103 1.255399 13 28230642 rs10712355 A AT 0.211941 0.788059 0.000415 0.841463 13 71296869 rs2249209 A G 0.409982 0.590018 0.000385 1.243197 13 74558505 rs12871414 T C 0.265293 0.734707 0.000094 0.752194 13 74804797 rs2039342 T C 0.110526 0.889474 0.000345 1.761241 13 98753461 rs545096 G C 0.465548 0.534452 0.000349 1.268761 13 99778655 rs35784338 CT C 0.199852 0.800148 0.000199 0.777428 13 101425653 rs9585503 G T 0.079322 0.920678 0.000250 0.503729 13 102622688 rs7339161 G T 0.306514 0.693486 0.000203 0.859200 14 35857405 rs61988300 G A 0.189617 0.810383 0.000315 0.790426 14 41523312 rs11157189 G A 0.498534 0.501466 0.000314 0.893382 14 65225452 rs58725048 C G 0.056880 0.943120 0.000269 1.285578 14 72891494 rs2238191 A C 0.428866 0.571134 0.000196 1.256746 14 72908102 rs2238187 G A 0.352272 0.647728 0.000008 1.442188 14 72934229 rs12587980 T C 0.374816 0.625184 0.000093 1.277281 14 76011690 rs2734265 G A 0.480501 0.519499 0.000178 1.168686 14 97526363 rs75607541 A T 0.050779 0.949221 0.000337 0.566281 15 27274425 rs149380649 C CT 0.065191 0.934809 0.000112 0.738484 15 33407307 rs17816808 G A 0.096916 0.903084 0.000295 1.377528 15 33908103 rs12593288 T C 0.206252 0.793748 0.000023 0.839689 15 33914240 rs16973353 A T 0.425332 0.574668 0.000307 1.140667 15 33916053 rs2229117 C G 0.133455 0.866545 0.000056 0.753496 15 46666881 rs1994195 C A 0.270838 0.729162 0.000295 0.880898 15 53279291 rs719715 G A 0.181209 0.818791 0.000321 0.711192 15 81412674 rs2683240 C T 0.237678 0.762322 0.000184 1.164944 15 89162709 rs112248718 A G 0.105913 0.894087 0.000406 1.241798 15 89165665 rs73451724 A G 0.038001 0.961999 0.000235 1.633115 16 4065412 rs12448453 G A 0.079483 0.920517 0.000148 1.385477 16 5898969 rs11647387 C A 0.250999 0.749001 0.000406 0.804925 16 6081670 rs8053942 T C 0.473297 0.526703 0.000304 1.121533 16 6653119 rs12934582 C T 0.096462 0.903538 0.000264 1.562484 16 27040235 rs2063839 A G 0.077891 0.922109 0.000272 0.703891 16 31392047 rs11574646 T C 0.219875 0.780125 0.000358 0.816824 16 31404502 rs2454907 A G 0.149476 0.850524 0.000304 0.758433 16 78624025 rs72803978 G A 0.065596 0.934404 0.000061 0.651614 16 78865144 rs68020681 G T 0.093881 0.906119 0.000404 0.750446 16 84006469 rs2250573 A C 0.112712 0.887288 0.000148 0.614458 16 85992829 rs11117428 C T 0.233426 0.766574 0.000318 0.861041 17 3110572 rs34259120 C A 0.446158 0.553842 0.000320 1.312096 17 9170408 rs34761447 T C 0.093770 0.906230 0.000026 0.766580 17 15548222 rs55821658 T C 0.054679 0.945321 0.000365 0.615661 17 18671675 rs55828488 A G 0.419792 0.580208 0.000284 1.241867 17 29737612 rs178840 A G 0.245600 0.754400 0.000075 0.704646 17 29740894 rs35054028 T G 0.087438 0.912562 0.000250 0.638460 17 31676083 rs59341815 C T 0.290909 0.709091 0.000294 1.271254 18 649311 rs9966612 A G 0.283148 0.716852 0.000210 0.827402 18 3899729 rs2667396 C T 0.468088 0.531912 0.000259 1.228547 18 9095227 rs16954792 C T 0.153181 0.846819 0.000368 0.711941 18 10016417 rs618909 A C 0.182016 0.817984 0.000193 1.375523 18 59747387 rs652473 C T 0.491954 0.508046 0.000338 1.348178 18 67208392 rs12958013 C T 0.135816 0.864184 0.000032 0.755900 18 67209524 rs34527658 AT A 0.237574 0.762426 0.000391 0.839220 18 76506592 rs35409638 T C 0.254573 0.745427 0.000390 1.430958 19 3058098 rs3217064 CT C 0.211726 0.788274 0.000188 0.784584 19 6533402 rs3097296 T C 0.364959 0.635041 0.000306 0.775131 19 15565046 rs9646651 A G 0.074145 0.925855 0.000323 0.642400 19 32023957 rs8105499 A C 0.304119 0.695881 0.000103 0.763075 19 44436733 rs8100011 G A 0.475911 0.524089 0.000334 0.832135 19 44492164 rs60744406 A G 0.415555 0.584445 0.000019 0.783341 19 50693096 rs648691 C T 0.434267 0.565733 0.000229 0.865794 19 53333975 rs10411226 G A 0.248705 0.751295 0.000092 1.246091 19 55500034 rs76616660 C T 0.114844 0.885156 0.000312 0.714110 19 57133633 rs35011777 T C 0.041190 0.958810 0.000365 2.311258 20 20344377 rs7270923 C A 0.100555 0.899445 0.000159 1.632986 20 38792298 rs6016275 T C 0.386489 0.613511 0.000192 1.294481 20 45355986 rs2076293 G A 0.465760 0.534240 0.000217 1.088991 20 52985158 rs6097944 G T 0.113465 0.886535 0.000284 0.798795 20 53043792 rs6023232 T G 0.191508 0.808492 0.000186 0.862778 21 20402128 rs62216866 G A 0.097434 0.902566 0.000367 0.853945 21 35423390 rs1986076 C T 0.368991 0.631009 0.000297 0.895966 21 39962001 rs9789875 T C 0.489381 0.510619 0.000299 0.852266 21 39963301 rs975846 A G 0.323369 0.676631 0.000268 1.326863 21 41321695 rs11701006 A G 0.233935 0.766065 0.000196 0.842328 21 43080428 rs2252109 A T 0.481328 0.518672 0.000043 1.207556 21 43086264 rs2849697 T C 0.458596 0.541404 0.000144 1.228411 21 46991937 rs76902403 A G 0.100782 0.899218 0.000155 0.578181 22 22564734 rs5757427 A T 0.351595 0.648405 0.000002 0.764406 22 22724951 rs7290963 T G 0.448669 0.551331 0.000072 1.306623 22 24407483 rs11090305 C T 0.186359 0.813641 0.000038 1.155463 22 44323597 rs139049 T C 0.410270 0.589730 0.000306 1.377674 22 44341300 rs17494724 A G 0.069631 0.930369 0.000157 1.803635 22 47986266 rs56813510 A C 0.165988 0.834012 0.000189 0.838987 22 49677464 rs62220604 A G 0.276036 0.723964 0.000036 0.908008

TABLE 3 Informative polymorphisms of the invention - 58 polymorphism panel. Frequency Frequency p-value for Chromsome Position SNP ID Allele 1 Allele 2 Allele 1 Allele 2 association OR 1 2998313 rs12745140 A G 0.088688543 0.911311457 0.0000317 2.2144 1 31624029 rs12083278 G C 0.295029481 0.704970519 0.00000243 1.8349 1 36374101 rs2765013 T C 0.086283231 0.913716769 0.000039 0.4296 1 63766718 rs112728381 T C 0.310873133 0.689126867 0.0000252 0.6017 1 87628173 rs10873821 T C 0.247508766 0.752491234 0.0000228 0.5706 2 36905013 rs6714112 A C 0.138077241 0.861922759 0.00000781 0.457 2 217524986 rs2270360 C A 0.265435195 0.734564805 0.0000245 0.5804 3 1093795 rs1504061 G C 0.050105352 0.949894648 0.0000716 2.5193 3 27188298 rs17317135 A G 0.048132554 0.951867446 0.0000641 0.3776 3 141408691 rs6440031 A G 0.084557 0.915443 0.0000714 2.188 4 5821877 rs3774881 C T 0.151610603 0.848389397 0.0000459 0.5358 4 5821922 rs3774882 G C 0.075889692 0.924110308 0.0000349 0.4206 4 27383278 rs6810404 A C 0.49098462 0.50901538 0.0000988 0.6344 4 44418592 rs35540967 C T 0.074930921 0.925069079 0.0000289 2.4621 4 69705994 rs115162070 A G 0.069850883 0.930149117 0.00000356 0.3716 4 112613026 rs112641600 T C 0.10467637 0.89532363 0.000058 0.4607 5 122832716 rs62377777 C T 0.219392587 0.780607413 0.0000653 0.5724 5 123950404 rs4240376 T G 0.201642654 0.798357346 0.000065 0.5706 5 142252549 rs10039856 T C 0.097201508 0.902798492 0.0000585 0.4621 5 173989338 rs2220543 A T 0.28983847 0.71016153 0.0000187 0.5793 5 180237828 rs113791144 T C 0.066435175 0.933564825 0.000144 2.2728 6 6925195 rs6933436 C A 0.283851708 0.716148292 0.0000983 1.6177 6 12216966 rs10755709 G A 0.300794985 0.699205015 0.00003 0.6017 6 18015447 rs140247774 T C 0.066938299 0.933061701 0.000047 2.5961 6 45704813 rs16873740 A T 0.118789191 0.881210809 0.0000296 2.0401 6 106326754 rs9386484 A T 0.236291943 0.763708057 0.00000617 0.5385 8 16790149 rs118072448 C T 0.076837933 0.923162067 0.0000235 0.4194 8 38821327 rs10808999 A G 0.133667804 0.866332196 0.0000884 1.8927 8 38897470 rs13282163 C A 0.083088415 0.916911585 0.0000739 0.4185 8 40181978 rs11779911 A C 0.334673592 0.665326408 0.0000841 0.6126 8 74268198 rs2010843 T C 0.468438061 0.531561939 0.0000556 1.5904 9 4329170 rs3895472 T C 0.0735178 0.9264822 0.0000235 2.4181 9 21131627 rs12236000 C G 0.076624262 0.923375738 0.0000452 0.409 9 81158113 rs7027911 A G 0.428022077 0.571977923 0.0000905 0.6281 10 9030308 rs71481792 A T 0.38112705 0.61887295 0.0000223 0.5863 10 37277870 rs2091431 A G 0.29138208 0.70861792 0.0000935 1.6209 10 37454397 rs1892429 G A 0.160418285 0.839581715 0.0000335 0.5273 10 44015051 rs10793436 T G 0.317754779 0.682245221 0.0000302 0.5969 10 54100345 rs1441121 A T 0.438240499 0.561759501 0.0000322 1.5904 11 2893867 rs10766439 A G 0.362550753 0.637449247 0.0000937 0.6376 12 8760610 rs11613792 G A 0.138547884 0.861452116 0.00000256 0.4686 12 106624953 rs12823094 A T 0.244075133 0.755924867 0.0000633 1.7006 13 74558505 rs12871414 T C 0.265293174 0.734706826 0.000094 0.6108 14 72908102 rs2238187 G A 0.352271894 0.647728106 0.00000821 1.7023 14 72934229 rs12587980 T C 0.374815784 0.625184216 0.0000933 1.573 15 33908103 rs12593288 T C 0.206251661 0.793748339 0.000023 0.5599 15 33916053 rs2229117 C G 0.133454893 0.866545107 0.0000561 0.5184 16 78624025 rs72803978 G A 0.065595834 0.934404166 0.0000612 0.3727 17 9170408 rs34761447 T C 0.093769913 0.906230087 0.0000262 0.4612 17 29737612 rs178840 A G 0.245600067 0.754399933 0.0000753 0.5886 18 67208392 rs12958013 C T 0.135815591 0.864184409 0.0000319 0.5148 19 44492164 rs60744406 A G 0.415555399 0.584444601 0.000019 1.6389 19 53333975 rs10411226 G A 0.248705364 0.751294636 0.0000923 0.5644 21 43080428 rs2252109 A T 0.481327711 0.518672289 0.0000428 0.6269 22 22564734 rs5757427 A T 0.351595363 0.648404637 0.00000237 1.804 22 22724951 rs7290963 T G 0.448668942 0.551331058 0.0000716 1.5872 22 24407483 rs11090305 C T 0.18635889 0.81364111 0.0000377 1.8386 22 49677464 rs62220604 A G 0.276036142 0.723963858 0.0000355 0.5927

TABLE 4 Informative polymorphisms used in genetic risk assessment of Example 5-64 polymorphism panel. All SNPs are from the COVID-19 Host Genetics Initiative meta-analysis of hospitalisation vs non-hospitalisation except for rs11385942 and rs657152, which are from Ellinghaus et al. (2020). Chromo- Reference Risk allele Risk allele Risk allele some ID allele (A1 allele) odds ratio frequency 1 rs12745140 G A 2.21 0.11 1 rs12083278 G C 1.83 0.70 1 rs2765013 T C 2.33 0.92 1 rs2274122 G A 1.78 0.80 1 rs10873821 T C 1.75 0.77 2 rs6714112 A C 2.19 0.86 2 rs2270360 C A 1.72 0.71 3 rs1504061 C G 2.52 0.06 3 rs17317135 A G 2.65 0.94 3 rs1868132 C T 1.97 0.10 3 rs6440031 A G 2.19 0.89 4 rs3774881 C T 1.87 0.85 4 rs3774882 G C 2.38 0.92 4 rs6810404 A C 1.58 0.51 4 rs35540967 T C 2.46 0.07 4 rs115162070 A G 2.69 0.92 4 rs11729561 C T 2.25 0.92 4 rs112641600 T C 2.17 0.90 5 rs62377777 C T 1.75 0.79 5 rs4240376 T G 1.75 0.80 5 rs10039856 T C 2.16 0.91 5 rs2220543 A T 1.73 0.71 5 rs113791144 C T 2.27 0.06 6 rs6933436 A C 1.62 0.28 6 rs10755709 G A 1.66 0.69 6 rs140247774 C T 2.60 0.06 6 rs16873740 T A 2.04 0.12 6 rs9386484 A T 1.86 0.75 8 rs118072448 C T 2.38 0.91 8 rs10808999 A G 1.89 0.86 8 rs13282163 C A 2.39 0.93 8 rs11779911 A C 1.63 0.66 8 rs2010843 T C 1.59 0.55 9 rs3895472 T C 2.42 0.91 9 rs12236000 C G 2.44 0.93 9 rs7027911 G A 1.59 0.44 10 rs71481792 T A 1.71 0.38 10 rs2091431 A G 1.62 0.71 10 rs1892429 G A 1.90 0.79 10 rs10793436 T G 1.68 0.68 10 rs1441121 A T 1.59 0.56 11 rs10766439 G A 1.57 0.39 12 rs11613792 G A 2.13 0.84 12 rs12823094 T A 1.70 0.26 13 rs1984162 A G 1.65 0.26 13 rs12871414 T C 1.64 0.72 14 rs2238187 A G 1.70 0.36 14 rs12587980 C T 1.54 0.39 15 rs12593288 T C 1.79 0.78 15 rs2229117 C G 1.93 0.87 16 rs72803978 G A 2.68 0.94 17 rs34761447 T C 2.17 0.89 17 rs178840 A G 1.70 0.75 18 rs12958013 C T 1.94 0.85 19 rs8105499 A C 1.62 0.69 19 rs60744406 A G 1.64 0.61 19 rs10411226 A G 1.77 0.24 21 rs2252109 T A 1.60 0.49 22 rs5757427 A T 1.80 0.63 22 rs7290963 G T 1.59 0.45 22 rs11090305 T C 1.84 0.18 22 rs62220604 A G 1.69 0.71 3 rs11385942 G GA 1.77 0.09 9 rs657152 C A 1.32 0.35

TABLE 5 New informative polymorphisms used in the development of the models described in Example 6. Reference Effect Frequency Frequency p-value for Chromosome Position SNP ID Allele Allele 1 2 association OR 95% CI 1 46618634 rs17102023 A G 1 0 0.5 1.33 0.63, 2.81 1 150271556 rs115492982 G A 1 0 0.01 2.46 1.23, 4.91 1 152684866 rs2224986 C T 0.91 0.09 0.8 0.98 0.85, 1.14 1 192526317 rs74508649 C T 1 0 0.9 1.04 0.47, 2.32 1 239197542 rs112317747 T C 0.97 0.03 0.05 1.26 1.00, 1.58 2 79895332 rs183569214 G A 1 0 0.7 0.72 0.15, 3.45 2 80029580 rs77764981 T C 1 0 0.6 1.29 0.54, 3.10 2 182353446 rs2034831 A C 0.94 0.06 0.02 1.22 1.03, 1.46 3 3184653 rs1705826 C G 0.63 0.37 0.5 1.03 0.94, 1.12 3 45841938 rs35896106 C T 0.92 0.08 0.04 1.17 1.01, 1.35 3 45900634 rs76374459 G C 0.94 0.06 0.03 1.2 1.02, 1.41 3 45908514 rs35652899 C G 0.93 0.07 0.04 1.17 1.00, 1.36 3 45916222 rs12639224 C T 0.73 0.27 0.7 1.02 0.93, 1.12 3 45916786 rs34901975 G A 0.89 0.11 0.09 1.12 0.98, 1.27 3 46018781 rs71615437 A G 0.92 0.08 0.1 1.12 0.97, 1.29 3 46049765 rs13433997 T C 0.88 0.12 0.1 1.1 0.97, 1.24 3 46180416 rs10510749 C T 0.91 0.09 0.9 0.99 0.85, 1.15 3 46222037 rs115102354 A G 0.95 0.05 0.7 0.96 0.79, 1.16 3 62936766 rs13062942 A G 0.64 0.36 0.09 0.92 0.84, 1.01 3 148718087 rs76488148 G T 0.96 0.04 0.03 1.25 1.02, 1.52 5 169590905 rs4478338 T G 0.92 0.08 0.3 1.08 0.93, 1.25 5 171480160 rs111265173 C T 1 0 1 0.97 0.35, 2.66 6 27604726 rs61611950 C T 0.99 0.01 0.8 0.92 0.56, 1.51 7 152960930 rs6967210 T C 0.99 0.01 0.3 1.17 0.86, 1.59 8 8730488 rs332040 G A 0.53 0.47 0.9 1 0.92, 1.09 9 27121456 rs71480372 A T 0.66 0.34 0.7 0.98 0.90, 1.08 9 29688719 rs74790577 A T 1 0 0.9 1.05 0.27, 4.03 10 123000638 rs5016035 T G 0.51 0.49 0.9 1 0.91, 1.10 12 56084466 rs7397549 T C 0.59 0.41 0.9 0.99 0.90, 1.09 13 63178476 rs2649134 C T 0.97 0.03 0.5 0.93 0.72, 1.19 14 77692036 rs144114696 G A 1 0 0.3 2.53  0.51, 12.44 15 45858905 rs77055952 A G 0.95 0.05 0.5 1.07 0.88, 1.29 15 48984345 rs74750712 T G 1 0 0.4 1.33 0.65, 2.69 16 10579876 rs72779789 G C 0.95 0.05 0.7 1.04 0.85, 1.26 16 49311043 rs145643452 G A 0.99 0.01 0.9 1.03 0.61, 1.74 17 80443309 rs9890316 G A 0.69 0.31 0.9 1.01 0.92, 1.10 18 30006171 rs142257532 T C 0.97 0.03 1 1.01 0.78, 1.30 20 39389409 rs56259900 A G 1 0 0.6 1.15 0.65, 2.04 20 60473717 rs76253189 C G 0.99 0.01 1 1.01 0.72, 1.42 21 44424444 rs75994231 C T 0.98 0.02 0.7 1.06 0.79, 1.43

TABLE 6 Surrogate markers for polymorphism provided in Table 3 (Part A) and additional polymorphisms used in Table 4 (Part B). Part C provides surrogate markers for rs115492982. Coordinate Primary SNP Proxy SNP (GRCh37/hg19) Alleles MAF Distance D′ R2 Correlated_Alleles Part A rs2765013 rs581459 chr1:36375110 (C/T) 0.0875 1009 1 1 C = C, T = T rs2765013 rs2791961 chr1:36371419 (G/C) 0.0885 −2682 1 0.9877 C = G, T = C rs2765013 rs2791962 chr1:36371168 (T/C) 0.0885 −2933 1 0.9877 C = T, T = C rs2765013 rs794222 chr1:36369024 (T/C) 0.0885 −5077 1 0.9877 C = T, T = C rs2765013 rs661233 chr1:36364716 (A/G) 0.0885 −9385 1 0.9877 C = A, T = G rs2765013 rs654688 chr1:36384215 (T/C) 0.0885 10114 1 0.9877 C = T, T = C rs2765013 rs647690 chr1:36363982 (G/C) 0.0885 −10119 1 0.9877 C = G, T = C rs2765013 rs636832 chr1:36363475 (G/A) 0.0885 −10626 1 0.9877 C = G, T = A rs2765013 rs620956 chr1:36362162 (T/C) 0.0885 −11939 1 0.9877 C = T, T = C rs2765013 rs653417 chr1:36356842 (G/T) 0.0885 −17259 1 0.9877 C = G, T = T rs2765013 rs2765012 chr1:36356457 (A/G) 0.0885 −17644 1 0.9877 C = A, T = G rs2765013 rs11263830 chr1:36345798 (G/A) 0.0885 −28303 1 0.9877 C = G, T = A rs2765013 rs811114 chr1:36345758 (G/A) 0.0885 −28343 1 0.9877 C = G, T = A rs2765013 rs11263843 chr1:36405929 (G/A) 0.0885 31828 1 0.9877 C = G, T = A rs2765013 rs12031138 chr1:36407806 (A/G) 0.0885 33705 1 0.9877 C = A, T = G rs2765013 rs6665591 chr1:36411737 (A/G) 0.0885 37636 1 0.9877 C = A, T = G rs2765013 rs12041193 chr1:36412760 (C/T) 0.0885 38659 1 0.9877 C = C, T = T rs2765013 rs67641270 chr1:36413329 (T/C) 0.0885 39228 1 0.9877 C = T, T = C rs2765013 rs142884229 chr1:36419258 (-/AGAGAATACAGGTGT) 0.0885 45157 1 0.9877 C = -, T = AGAGAATACAGGTGT rs2765013 rs716926 chr1:36424476 (T/C) 0.0885 50375 1 0.9877 C = T, T = C rs2765013 rs716925 chr1:36424517 (A/G) 0.0885 50416 1 0.9877 C = A, T = G rs2765013 rs10796876 chr1:36424985 (A/G) 0.0885 50884 1 0.9877 C = A, T = G rs2765013 rs60867325 chr1:36429590 (-/G) 0.0885 55489 1 0.9877 C = -, T = G rs2765013 rs645864 chr1:36430941 (C/A) 0.0885 56840 1 0.9877 C = C, T = A rs2765013 rs649152 chr1:36438649 (T/G) 0.0885 64548 1 0.9877 C = T, T = G rs2765013 rs709309 chr1:36441148 (G/T) 0.0885 67047 1 0.9877 C = G, T = T rs2765013 rs686650 chr1:36441613 (A/G) 0.0885 67512 1 0.9877 C = A, T = G rs2765013 rs682686 chr1:36443778 (T/A) 0.0885 69677 1 0.9877 C = T, T = A rs2765013 rs665521 chr1:36445837 (T/C) 0.0885 71736 1 0.9877 C = T, T = C rs2765013 rs630364 chr1:36449304 (C/T) 0.0885 75203 1 0.9877 C = C, T = T rs2765013 rs625306 chr1:36454016 (T/C) 0.0885 79915 1 0.9877 C = T, T = C rs2765013 rs688833 chr1:36455600 (C/T) 0.0885 81499 1 0.9877 C = C, T = T rs2765013 rs112607939 chr1:36460189 (-/CTAT) 0.0885 86088 1 0.9877 C = -, T = CTAT rs2765013 rs688191 chr1:36460681 (G/A) 0.0885 86580 1 0.9877 C = G, T = A rs2765013 rs7542179 chr1:36461122 (G/A) 0.0885 87021 1 0.9877 C = G, T = A rs2765013 rs56198971 chr1:36403174 (T/C) 0.0895 29073 1 0.9756 C = T, T = C rs2765013 rs644095 chr1:36363172 (C/T) 0.0875 −10929 0.9875 0.9752 C = C, T = T rs2765013 rs111375913 chr1:36403173 (-/C) 0.0875 29072 0.9875 0.9752 C = -, T = C rs2765013 rs645383 chr1:36373823 (C/G) 0.0855 −278 1 0.9751 C = C, T = G rs2765013 rs35467166 chr1:36410818 (-/C) 0.0865 36717 0.9874 0.9628 C = -, T = C rs2765013 rs663163 chr1:36390527 (A/G) 0.0915 16426 1 0.9524 C = A, T = G rs2765013 rs685370 chr1:36443214 (A/G) 0.0915 69113 1 0.9524 C = A, T = G rs2765013 rs614235 chr1:36450565 (G/A) 0.0944 76464 1 0.9193 C = G, T = A rs2765013 rs201615643 chr1:36468742 (-/T) 0.0934 94641 0.9875 0.9069 C = -, T = T rs2765013 rs683622 chr1:36443569 (A/G) 0.0755 69468 1 0.8525 C = A, T = G rs2765013 rs631202 chr1:36449099 (G/C) 0.0755 74998 1 0.8525 C = G, T = C rs2765013 rs645123 chr1:36458075 (T/C) 0.0755 83974 1 0.8525 C = T, T = C rs2765013 rs72661616 chr1:36370512 (G/A) 0.0746 −3589 1 0.8404 C = G, T = A rs2765013 rs55762724 chr1:36382702 (C/T) 0.0746 8601 1 0.8404 C = C, T = T rs2765013 rs72661613 chr1:36364781 (A/G) 0.0746 −9320 1 0.8404 C = A, T = G rs2765013 rs199596553 chr1:36357062 (T/-) 0.0746 −17039 1 0.8404 C = T, T = - rs2765013 rs72661623 chr1:36398368 (G/A) 0.0746 24267 1 0.8404 C = G, T = A rs2765013 rs116757422 chr1:36349560 (G/A) 0.0746 −24541 1 0.8404 C = G, T = A rs2765013 rs72661625 chr1:36401003 (T/C) 0.0746 26902 1 0.8404 C = T, T = C rs2765013 rs79303353 chr1:36402056 (G/A) 0.0746 27955 1 0.8404 C = G, T = A rs2765013 rs72661628 chr1:36406547 (A/G) 0.0746 32446 1 0.8404 C = A, T = G rs2765013 rs74879824 chr1:36413117 (T/A) 0.0746 39016 1 0.8404 C = T, T = A rs2765013 rs72661631 chr1:36417453 (A/G) 0.0746 43352 1 0.8404 C = A, T = G rs2765013 rs72661632 chr1:36420715 (G/A) 0.0746 46614 1 0.8404 C = G, T = A rs2765013 rs72661636 chr1:36426483 (T/C) 0.0746 52382 1 0.8404 C = T, T = C rs2765013 rs142370407 chr1:36430202 (C/G) 0.0746 56101 1 0.8404 C = C, T = G rs2765013 rs584873 chr1:36431806 (C/A) 0.0746 57705 1 0.8404 C = C, T = A rs2765013 rs142324044 chr1:36433070 (C/T) 0.0746 58969 1 0.8404 C = C, T = T rs2765013 rs72661640 chr1:36447232 (T/G) 0.0746 73131 1 0.8404 C = T, T = G rs2765013 rs72661641 chr1:36448130 (A/G) 0.0746 74029 1 0.8404 C = A, T = G rs2765013 rs72661643 chr1:36451599 (A/G) 0.0746 77498 1 0.8404 C = A, T = G rs2765013 rs72661644 chr1:36453894 (G/A) 0.0746 79793 1 0.8404 C = G, T = A rs2765013 rs72661646 chr1:36454954 (A/T) 0.0746 80853 1 0.8404 C = A, T = T rs2765013 rs72661655 chr1:36475401 (G/T) 0.0746 101300 1 0.8404 C = G, T = T rs2765013 rs138030683 chr1:36353261 (-/T) 0.0755 −20840 0.9856 0.8281 C = -, T = T rs2765013 rs148983758 chr1:36336826 (T/-) 0.0755 −37275 0.9856 0.8281 C = T, T = - rs2765013 rs629773 chr1:36431561 (A/G) 0.1064 57460 1 0.8054 C = A, T = G rs112728381 rs12732999 chr1:63766723 (G/A) 0.3241 5 1 0.9597 C = G, T = A rs10873821 rs7550642 chr1:87629310 (G/A) 0.2227 1137 1 1 C = G, T = A rs6714112 rs6716934 chr2:36912284 (A/T) 0.1282 7271 1 0.9646 C = A, A = T rs1504061 rs1504061 chr3:1093795 (C/G) 0.0487 0 1 1 C = C, G = G rs1504061 rs1312543601 chr3:1094816 (-/T) 0.0567 1021 1 0.8525 C = -, G = T rs1504061 rs55975362 chr3:1103832 (G/C) 0.0577 10037 1 0.8369 C = G, G = C rs1504061 rs72993835 chr3:1104218 (T/C) 0.0577 10423 1 0.8369 C = T, G = C rs1504061 rs111403218 chr3:1104296 (C/T) 0.0577 10501 1 0.8369 C = C, G = T rs1504061 rs142136066 chr3:1104395 (T/G) 0.0577 10600 1 0.8369 C = T, G = G rs1504061 rs116727350 chr3:1104957 (G/A) 0.0577 11162 1 0.8369 C = G, G = A rs1504061 rs72993828 chr3:1097969 (T/G) 0.0586 4174 1 0.8218 C = T, G = G rs1504061 rs72993831 chr3:1099234 (A/G) 0.0586 5439 1 0.8218 C = A, G = G rs1504061 rs72993838 chr3:1106164 (G/A) 0.0586 12369 1 0.8218 C = G, G = A rs17317135 rs9863368 chr3:27253521 (T/C) 0.0567 65223 1 0.947 G = C, A = T rs3774881 rs148165034 chr4:5822073 (AAC/-) 0.1412 196 1 0.9838 T = AAC, C = - rs3774881 rs6846920 chr4:5827911 (A/G) 0.1471 6034 0.9104 0.8027 T = A, C = G rs3774881 rs11318332 chr4:5827975 (A/-) 0.1451 6098 0.9025 0.8015 T = A, C = - rs6810404 rs3069341 chr4:27384266 (AGC/-) 0.4791 988 1 0.9921 C = AGC, A = - rs6810404 rs7659968 chr4:27383850 (G/A) 0.4831 572 1 0.9764 C = G, A = A rs6810404 rs9291496 chr4:27370190 (A/G) 0.4851 −13088 0.9957 0.8523 C = A, A = G rs35540967 rs17539340 chr4:44429579 (A/G) 0.0765 10987 1 1 T = A, C = G rs112641600 rs116432808 chr4:112626500 (T/C) 0.0934 13474 0.9883 0.9767 C = T, T = C rs112641600 rs72680134 chr4:112627204 (C/T) 0.0934 14178 0.9883 0.9767 C = C, T = T rs112641600 rs72680150 chr4:112711302 (A/G) 0.0934 98276 0.9765 0.9536 C = A, T = G rs112641600 rs147521731 chr4:112680269 (G/A) 0.0964 67243 0.9765 0.9209 C = G, T = A rs62377777 rs72080072 chr5:122835924 (TATAAG/-) 0.2356 3208 1 0.9891 T = TATAAG, C = - rs62377777 rs55893406 chr5:122837233 (T/A) 0.2346 4517 1 0.9836 T = T, C = A rs62377777 rs55914925 chr5:122834333 (G/A) 0.2336 1617 1 0.9782 T = G, C = A rs62377777 rs2036321 chr5:122838501 (C/T) 0.2336 5785 1 0.9782 T = C, C = T rs62377777 rs2173683 chr5:122841745 (C/A) 0.2336 9029 1 0.9782 T = C, C = A rs62377777 rs10593358 chr5:122845970 (CAAGC/-) 0.2336 13254 1 0.9782 T = CAAGC, C = - rs62377777 rs1392437 chr5:122821063 (C/T) 0.2336 −11653 0.9888 0.9564 T = C, C = T rs62377777 rs17164879 chr5:122863961 (C/T) 0.2237 31245 0.9942 0.9138 T = C, C = T rs62377777 rs12519921 chr5:122830670 (G/A) 0.2078 −2046 1 0.8416 T = G, C = A rs62377777 rs62377776 chr5:122814546 (T/G) 0.2107 −18170 0.9814 0.8254 T = T, C = G rs62377777 rs12517980 chr5:122813888 (T/C) 0.2117 −18828 0.9754 0.8201 T = T, C = C rs62377777 rs6595453 chr5:122814897 (T/C) 0.2117 −17819 0.9692 0.8097 T = T, C = C rs4240376 rs10066524 chr5:123950880 (G/A) 0.2207 476 1 0.9429 G = G, T = A rs4240376 rs10068590 chr5:123951944 (C/A) 0.1998 1540 0.9874 0.9117 G = C, T = A rs4240376 rs12520962 chr5:123953224 (G/C) 0.2018 2820 0.9688 0.8886 G = G, T = C rs4240376 rs4580771 chr5:123954814 (T/C) 0.2018 4410 0.9688 0.8886 G = T, T = C rs4240376 rs6871217 chr5:123960447 (G/A) 0.1988 10043 0.9747 0.8828 G = G, T = A rs4240376 rs10428701 chr5:123960974 (C/T) 0.1988 10570 0.9747 0.8828 G = C, T = T rs4240376 rs12514836 chr5:123957328 (A/G) 0.2068 6924 0.933 0.8498 G = A, T = G rs4240376 rs62372143 chr5:123957443 (A/G) 0.2068 7039 0.933 0.8498 G = A, T = G rs4240376 rs62372144 chr5:123957610 (All) 0.2068 7206 0.933 0.8498 G = A, T = T rs4240376 rs66500836 chr5:123957985 (C/G) 0.2068 7581 0.933 0.8498 G = C, T = G rs4240376 rs6899090 chr5:123958033 (A/G) 0.2068 7629 0.933 0.8498 G = A, T = G rs4240376 rs6859251 chr5:123958110 (G/A) 0.2068 7706 0.933 0.8498 G = G, T = A rs4240376 rs6859613 chr5:123958332 (G/C) 0.2068 7928 0.933 0.8498 G = G, T = C rs4240376 rs12513982 chr5:123959748 (T/A) 0.2068 9344 0.933 0.8498 G = T, T = A rs4240376 rs373774956 chr5:123961537 (TTTGTTTG/-) 0.2068 11133 0.933 0.8498 G = TTTGTTTG, T = - rs4240376 rs6893169 chr5:123960800 (T/A) 0.2058 10396 0.9327 0.844 G = T, T = A rs4240376 rs11241757 chr5:123961500 (T/C) 0.2058 11096 0.9327 0.844 G = T, T = C rs4240376 rs12516428 chr5:123959806 (A/G) 0.2048 9402 0.9323 0.8383 G = A, T = G rs4240376 rs10052511 chr5:123963652 (C/T) 0.1889 13248 0.98 0.8375 G = C, T = T rs4240376 rs71574121 chr5:123964265 (GA/-) 0.1849 13861 0.9796 0.8152 G = GA, T = - rs2220543 rs7720656 chr5:173996282 (A/G) 0.3012 6944 0.9952 0.9672 T = G, A = A rs2220543 rs1387768 chr5:173993166 (A/G) 0.2803 3828 1 0.9254 T = A, A = G rs2220543 rs1387769 chr5:173993252 (C/A) 0.2803 3914 1 0.9254 T = C, A = A rs2220543 rs4868427 chr5:173992056 (A/T) 0.2813 2718 0.995 0.9206 T = T, A = A rs113791144 rs117101214 chr5:180237845 (C/T) 0.0835 17 1 1 C = C, T = T rs113791144 rs147634845 chr5:180237902 (C/A) 0.0835 74 1 1 C = C, T = A rs113791144 rs78102637 chr5:180238393 (G/A) 0.0835 565 1 1 C = G, T = A rs113791144 rs11544558 chr5:180235737 (C/A) 0.0865 −2091 1 0.9624 C = C, T = A rs113791144 rs75268547 chr5:180238308 (G/T) 0.0845 480 0.987 0.9617 C = G, T = T rs113791144 rs112702671 chr5:180220930 (C/T) 0.0885 −16898 0.9869 0.9143 C = C, T = T rs113791144 rs10577599 chr5:180216905 (AT/-) 0.0845 −20923 0.961 0.9116 C = AT, T = - rs113791144 rs76809244 chr5:180216077 (C/G) 0.0855 −21751 0.9609 0.9 C = C, T = G rs113791144 rs145796183 chr5:180238591 (1/-) 0.0686 763 1 0.8083 C = T, T = - rs113791144 rs10040542 chr5:180242178 (C/T) 0.0686 4350 1 0.8083 C = C, T = T rs10755709 rs7356945 chr6:12217422 (C/T) 0.3022 456 0.9525 0.8988 A = C, G = T rs16873740 rs35926878 chr6:45705862 (G/A) 0.1183 1049 1 1 T = G, A = A rs13282163 rs55750326 chr8:38949033 (C/-) 0.0875 51563 1 0.9877 A = C, C = - rs12236000 rs10964856 chr9:21131785 (A/G) 0.0855 158 1 0.9873 G = A, C = G rs2091431 rs2505150 chr10:37278930 (A/G) 0.3211 1060 1 1 A = A, G = G rs2091431 rs10764117 chr10:37284272 (A/G) 0.325 6402 1 0.982 A = A, G = G rs2091431 rs2505172 chr10:37286042 (C/A) 0.325 8172 1 0.982 A = C, G = A rs2091431 rs2459442 chr10:37292750 (G/A) 0.3241 14880 0.9954 0.9774 A = A, G = G rs2091431 rs2459421 chr10:37303511 (C/T) 0.3231 25641 0.9863 0.9639 A = T, G = C rs2091431 rs138295864 chr10:37302778 (-/A) 0.3211 24908 0.9818 0.9639 A = A, G = - rs2091431 rs35067257 chr10:37282717 (1/-) 0.336 4847 1 0.9346 A = T, G = - rs2091431 rs1914151 chr10:37304072 (C/A) 0.338 26202 0.9766 0.8835 A = A, G = C rs2091431 rs1914152 chr10:37304128 (A/G) 0.338 26258 0.9766 0.8835 A = G, G = A rs2091431 rs2459426 chr10:37307089 (G/A) 0.337 29219 0.972 0.8791 A = A, G = G rs2091431 rs2459429 chr10:37308202 (C/T) 0.337 30332 0.972 0.8791 A = T, G = C rs2091431 rs2459433 chr10:37311324 (A/G) 0.337 33454 0.972 0.8791 A = G, G = A rs2091431 rs2505166 chr10:37318114 (C/T) 0.337 40244 0.972 0.8791 A = T, G = C rs2091431 rs11011000 chr10:37328362 (A/G) 0.337 50492 0.972 0.8791 A = G, G = A rs2091431 rs2459440 chr10:37338386 (C/T) 0.337 60516 0.972 0.8791 A = T, G = C rs2091431 rs2505174 chr10:37338614 (G/A) 0.337 60744 0.972 0.8791 A = A, G = G rs2091431 rs2459441 chr10:37340271 (T/C) 0.337 62401 0.972 0.8791 A = C, G = T rs2091431 rs34886927 chr10:37345130 (T/-) 0.337 67260 0.972 0.8791 A = -, G = T rs2091431 rs10827752 chr10:37318895 (C/T) 0.338 41025 0.9719 0.8751 A = T, G = C rs2091431 rs12221175 chr10:37327753 (C/T) 0.338 49883 0.9719 0.8751 A = T, G = C rs2091431 rs2505164 chr10:37317344 (C/A) 0.339 39474 0.9719 0.8711 A = A, G = C rs2091431 rs2459434 chr10:37311831 (G/T) 0.337 33961 0.9626 0.8623 A = T, G = G rs1892429 rs1200880 chr10:37450374 (T/C) 0.2624 −4023 1 0.9 A = T, G = C rs1892429 rs1767366 chr10:37494647 (G/A) 0.2624 40250 1 0.9 A = G, G = A rs1892429 rs1933748 chr10:37498227 (C/T) 0.2624 43830 1 0.9 A = C, G = T rs1892429 rs2490107 chr10:37484946 (G/A) 0.2634 30549 1 0.8954 A = G, G = A rs1892429 rs1200876 chr10:37505141 (G/A) 0.2634 50744 1 0.8954 A = G, G = A rs1892429 rs1148258 chr10:37506384 (G/A) 0.2634 51987 1 0.8954 A = G, G = A rs1892429 rs138860607 chr10:37515709 (AGCAGCTATACCATTTTTCATT/-) 0.2634 61312 1 0.8954 A = AGCAGCTATACCATTTTTCATT, G = - rs1892429 rs1200857 chr10:37521828 (A/C) 0.2634 67431 1 0.8954 A = A, G = C rs1892429 rs1148264 chr10:37527168 (A/T) 0.2634 72771 1 0.8954 A = A, G = T rs1892429 rs1767387 chr10:37536757 (C/T) 0.2634 82360 1 0.8954 A = C, G = T rs1892429 rs1200875 chr10:37505192 (C/T) 0.2644 50795 1 0.8908 A = C, G = T rs1892429 rs2765819 chr10:37525622 (G/T) 0.2644 71225 1 0.8908 A = G, G = T rs1892429 rs1711240 chr10:37378880 (C/T) 0.2604 −75517 0.9446 0.8113 A = C, G = T rs1441121 rs1441123 chr10:54101139 (T/G) 0.4334 794 1 0.996 A = T, T = G rs1441121 rs73331255 chr10:54105039 (C/G) 0.4324 4694 1 0.9919 A = C, T = G rs1441121 rs1372107 chr10:54114458 (G/C) 0.4404 14113 0.9959 0.9681 A = G, T = C rs1441121 rs1441124 chr10:54114916 (G/A) 0.4404 14571 0.9959 0.9681 A = G, T = A rs1441121 rs11001719 chr10:54108610 (C/T) 0.4394 8265 0.9918 0.9641 A = C, T = T rs1441121 rs11814479 chr10:54108930 (T/G) 0.4394 8585 0.9918 0.9641 A = T, T = G rs1441121 rs11001721 chr10:54109544 (A/G) 0.4394 9199 0.9918 0.9641 A = A, T = G rs1441121 rs11001723 chr10:54110313 (C/T) 0.4394 9968 0.9918 0.9641 A = C, T = T rs1441121 rs7079513 chr10:54110965 (TG) 0.4394 10620 0.9918 0.9641 A = T, T = G rs1441121 rs7075951 chr10:54111095 (A/G) 0.4394 10750 0.9918 0.9641 A = A, T = G rs1441121 rs7904997 chr10:54111864 (A/T) 0.4394 11519 0.9918 0.9641 A = A, T = T rs1441121 rs10824390 chr10:54110416 (A/T) 0.4404 10071 0.9918 0.9602 A = A, T = T rs1441121 rs894104 chr10:54106670 (T/C) 0.4384 6325 0.9878 0.9601 A = T, T = C rs1441121 rs10762708 chr10:54108307 (C/G) 0.4384 7962 0.9878 0.9601 A = C, T = G rs1441121 rs10762712 chr10:54112284 (T/C) 0.4364 11939 0.9797 0.9521 A = T, T = C rs1441121 rs10740457 chr10:54112762 (A/G) 0.4364 12417 0.9797 0.9521 A = A, T = G rs10766439 rs2078786 chr11:2896128 (A/G) 0.3678 2261 1 0.9872 A = A, G = G rs10766439 rs11024404 chr11:2894452 (G/T) 0.3887 585 1 0.9034 A = G, G = T rs10766439 rs148588273 chr11:2899462 (-/C) 0.3986 5595 0.9955 0.8587 A = -, G = C rs10766439 rs10766443 chr11:2899586 (T/C) 0.3986 5719 0.9955 0.8587 A = T, G = C rs10766439 rs10766442 chr11:2899538 (C/T) 0.3976 5671 0.991 0.8544 A = C, G = T rs10766439 rs4929954 chr11:2900383 (C/G) 0.3966 6516 0.9865 0.8502 A = C, G = G rs12823094 rs35769445 chr12:106625862 (G/C) 0.2396 909 0.9945 0.9837 T = G, A = C rs2238187 rs2283380 chr14:72909738 (G/A) 0.3946 1636 0.924 0.8326 A = G, G = A rs12587980 rs917428 chr14:72935767 (C/T) 0.4006 1538 1 0.9959 C = C, T = T rs2229117 rs35242916 chr15:33917374 (C/T) 0.1332 1321 1 1 G = C, C = T rs2229117 rs34638660 chr15:33919774 (C/T) 0.1332 3721 1 1 G = C, C = T rs2229117 rs4780137 chr15:33922609 (T/A) 0.1332 6556 1 1 G = T, C = A rs2229117 rs4780138 chr15:33922983 (G/A) 0.1332 6930 1 1 G = G, C = A rs2229117 rs71462874 chr15:33924037 (G/A) 0.1332 7984 1 1 G = G, C = A rs2229117 rs35203574 chr15:33928785 (A/G) 0.1342 12732 1 0.9915 G = A, C = G rs2229117 rs36020093 chr15:33919750 (A/G) 0.1362 3697 1 0.9747 G = A, C = G rs2229117 rs2291730 chr15:33923690 (C/T) 0.1362 7637 1 0.9747 G = C, C = T rs2229117 rs3816940 chr15:33925062 (G/A) 0.1362 9009 1 0.9747 G = G, C = A rs2229117 rs12901506 chr15:33929755 (G/C) 0.1511 13702 1 0.8634 G = G, C = C rs2229117 rs71462875 chr15:33931313 (A/G) 0.1511 15260 1 0.8634 G = A, C = G rs72803978 rs76614455 chr16:78633493 (G/A) 0.0527 9468 0.98 0.873 A = G, G = A rs72803978 rs138270756 chr16:78640700 (-/AAT) 0.0626 16675 0.9448 0.8175 A = -, G = AAT rs34761447 rs35985527 chr17:9172769 (G/A) 0.1044 2361 0.9884 0.8843 C = G, T = A rs34761447 rs12952893 chr17:9176588 (G/C) 0.0994 6180 0.9306 0.8277 C = G, T = C rs34761447 rs7215786 chr17:9172095 (T/C) 0.1113 1687 0.9883 0.8224 C = T, T = C rs34761447 rs35880517 chr17:9174045 (G/A) 0.1004 3637 0.9305 0.8185 C = G, T = A rs34761447 rs35306109 chr17:9174082 (A/G) 0.1004 3674 0.9305 0.8185 C = A, T = G rs60744406 rs397964 chr19:44493969 (A/T) 0.4294 1805 1 1 A = T, G = A rs10411226 rs1974832 chr19:53333465 (G/C) 0.2247 −510 0.9943 0.9886 G = G, A = C rs5757427 rs2156880 chr22:22570271 (A/G) 0.3598 5537 1 0.9914 A = A, T = G rs5757427 rs11376968 chr22:22567202 (-/A) 0.3549 2468 1 0.9871 A = -, T = A rs5757427 rs2330040 chr22:22567762 (G/A) 0.3618 3028 1 0.9829 A = G, T = A rs5757427 rs5750729 chr22:22566976 (A/G) 0.3549 2242 0.9869 0.9614 A = A, T = G rs5757427 rs1007312 chr22:22569758 (A/C) 0.3539 5024 0.9869 0.9572 A = A, T = C rs5757427 rs738876 chr22:22568531 (C/T) 0.3559 3797 0.9826 0.9572 A = C, T = T rs5757427 rs5750739 chr22:22568820 (T/C) 0.3559 4086 0.9826 0.9572 A = T, T = C rs5757427 rs6001415 chr22:22569640 (T/G) 0.3559 4906 0.9826 0.9572 A = T, T = G rs5757427 rs5757477 chr22:22572904 (G/A) 0.3648 8170 0.9913 0.9534 A = G, T = A rs5757427 rs111384644 chr22:22565774 (-/CAGG) 0.3569 1040 0.9783 0.953 A = -, T = CAGG rs5757427 rs5757443 chr22:22566563 (T/G) 0.3579 1829 0.974 0.9488 A = T, T = G rs5757427 rs738874 chr22:22567948 (A/G) 0.3579 3214 0.974 0.9488 A = A, T = G rs5757427 rs738875 chr22:22568014 (G/T) 0.3579 3280 0.974 0.9488 A = G, T = T rs5757427 rs5750741 chr22:22569448 (T/A) 0.3588 4714 0.974 0.9446 A = T, T = A rs5757427 rs762464 chr22:22574458 (G/C) 0.3588 9724 0.9697 0.9362 A = G, T = C rs5757427 rs5757469 chr22:22570658 (A/G) 0.3608 5924 0.9696 0.928 A = A, T = G rs5757427 rs1029267 chr22:22571456 (T/A) 0.3608 6722 0.9696 0.928 A = T, T = A rs5757427 rs1029270 chr22:22571681 (G/C) 0.3608 6947 0.9696 0.928 A = G, T = C rs5757427 rs4599223 chr22:22571981 (C/T) 0.3608 7247 0.9696 0.928 A = C, T = T rs5757427 rs5757486 chr22:22574248 (A/G) 0.3608 9514 0.9696 0.928 A = A, T = G rs5757427 rs35158064 chr22:22572122 (T/-) 0.3618 7388 0.9695 0.9239 A = T, T = - rs5757427 rs1023418 chr22:22572199 (T/C) 0.3618 7465 0.9695 0.9239 A = T, T = C rs5757427 rs5750745 chr22:22572812 (C/T) 0.3618 8078 0.9695 0.9239 A = C, T = T rs5757427 rs5750746 chr22:22572822 (G/A) 0.3618 8088 0.9695 0.9239 A = G, T = A rs5757427 rs968897 chr22:22570821 (C/T) 0.3598 6087 0.9653 0.9238 A = C, T = T rs5757427 rs5750749 chr22:22573209 (T/C) 0.3628 8475 0.9695 0.9198 A = T, T = C rs5757427 rs5750750 chr22:22573365 (C/T) 0.3628 8631 0.9695 0.9198 A = C, T = T rs5757427 rs5750751 chr22:22573414 (G/A) 0.3628 8680 0.9695 0.9198 A = G, T = A rs5757427 rs5757482 chr22:22573837 (C/T) 0.3628 9103 0.9695 0.9198 A = C, T = T rs5757427 rs5757483 chr22:22573878 (C/A) 0.3628 9144 0.9695 0.9198 A = C, T = A rs5757427 rs5757484 chr22:22573973 (C/G) 0.3628 9239 0.9695 0.9198 A = C, T = G rs5757427 rs5757485 chr22:22574035 (T/C) 0.3628 9301 0.9695 0.9198 A = T, T = C rs5757427 rs5750752 chr22:22574105 (G/A) 0.3628 9371 0.9695 0.9198 A = G, T = A rs5757427 rs762465 chr22:22574855 (C/T) 0.3618 10121 0.9652 0.9156 A = C, T = T rs5757427 rs1029269 chr22:22571612 (G/A) 0.3887 6878 0.9682 0.8217 A = G, T = A rs7290963 rs7287541 chr22:22725057 (T/G) 0.4433 106 1 0.992 G = T, T = G rs11090305 rs9608231 chr22:24415817 (A/T) 0.2157 8334 0.9823 0.9426 T = T, C = A rs11090305 rs6004044 chr22:24422166 (A/G) 0.2157 14683 0.9823 0.9426 T = G, C = A rs11090305 rs873833 chr22:24427878 (G/A) 0.2157 20395 0.9823 0.9426 T = A, C = G rs11090305 rs5996663 chr22:24429241 (C/T) 0.2157 21758 0.9823 0.9426 T = T, C = C rs11090305 rs2282475 chr22:24438047 (A/G) 0.2157 30564 0.9823 0.9426 T = G, C = A rs11090305 rs5751798 chr22:24443473 (T/C) 0.2157 35990 0.9823 0.9426 T = C, C = T rs11090305 rs2070467 chr22:24452885 (A/G) 0.2157 45402 0.9823 0.9426 T = G, C = A rs11090305 rs5760179 chr22:24411653 (C/T) 0.2147 4170 0.9822 0.9369 T = T, C = C rs11090305 rs6004042 chr22:24420722 (C/T) 0.2147 13239 0.9822 0.9369 T = T, C = C rs11090305 rs2267053 chr22:24457197 (C/T) 0.2147 49714 0.9822 0.9369 T = T, C = C rs11090305 rs2051198 chr22:24465672 (A/G) 0.2147 58189 0.9822 0.9369 T = G, C = A rs11090305 rs4822469 chr22:24425114 (C/G) 0.2157 17631 0.9764 0.9313 T = G, C = C rs11090305 rs2283807 chr22:24472270 (A/G) 0.2157 64787 0.9764 0.9313 T = G, C = A rs11090305 rs2000470 chr22:24488861 (C/T) 0.2157 81378 0.9764 0.9313 T = T, C = C rs11090305 rs5751803 chr22:24489649 (T/C) 0.2157 82166 0.9764 0.9313 T = C, C = T rs11090305 rs5760205 chr22:24490529 (C/T) 0.2157 83046 0.9764 0.9313 T = T, C = C rs11090305 rs2236622 chr22:24492061 (T/C) 0.2157 84578 0.9764 0.9313 T = C, C = T rs11090305 rs176156 chr22:24500866 (G/C) 0.2157 93383 0.9764 0.9313 T = G, C = C rs11090305 rs112272 chr22:24510620 (C/T) 0.2157 103137 0.9764 0.9313 T = C, C = T rs11090305 rs2267059 chr22:24525711 (T/C) 0.2157 118228 0.9764 0.9313 T = T, C = C rs11090305 rs2003756 chr22:24527749 (T/C) 0.2157 120266 0.9764 0.9313 T = T, C = C rs11090305 rs6519499 chr22:24532468 (T/C) 0.2157 124985 0.9764 0.9313 T = T, C = C rs11090305 rs2001105 chr22:24535559 (T/C) 0.2157 128076 0.9764 0.9313 T = T, C = C rs11090305 rs2267060 chr22:24535962 (G/A) 0.2157 128479 0.9764 0.9313 T = G, C = A rs11090305 rs5760218 chr22:24541432 (T/C) 0.2157 133949 0.9764 0.9313 T = T, C = C rs11090305 rs5760221 chr22:24542636 (T/A) 0.2157 135153 0.9764 0.9313 T = T, C = A rs11090305 rs2267062 chr22:24544482 (G/T) 0.2157 136999 0.9764 0.9313 T = G, C = T rs11090305 rs2267063 chr22:24544607 (T/C) 0.2157 137124 0.9764 0.9313 T = T, C = C rs11090305 rs2267064 chr22:24544632 (T/G) 0.2157 137149 0.9764 0.9313 T = T, C = G rs11090305 rs2267068 chr22:24550303 (T/C) 0.2157 142820 0.9764 0.9313 T = T, C = C rs11090305 rs915595 chr22:24551909 (T/G) 0.2157 144426 0.9764 0.9313 T = T, C = G rs11090305 rs879756 chr22:24552872 (C/A) 0.2157 145389 0.9764 0.9313 T = C, C = A rs11090305 rs6519501 chr22:24556507 (T/C) 0.2157 149024 0.9764 0.9313 T = T, C = C rs11090305 rs2267070 chr22:24558318 (T/C) 0.2157 150835 0.9764 0.9313 T = T, C = C rs11090305 rs5996668 chr22:24575952 (T/C) 0.2157 168469 0.9764 0.9313 T = T, C = C rs11090305 rs9624412 chr22:24585313 (A/G) 0.2157 177830 0.9764 0.9313 T = A, C = G rs11090305 rs141628202 chr22:24474904 (ATC/-) 0.2167 67421 0.9706 0.9258 T = -, C = ATC rs11090305 rs5844585 chr22:24483878 (T/-) 0.2167 76395 0.9706 0.9258 T = -, C = T rs11090305 rs8137732 chr22:24567031 (A/G) 0.2167 159548 0.9706 0.9258 T = A, C = G rs11090305 rs8137222 chr22:24576159 (G/A) 0.2167 168676 0.9706 0.9258 T = G, C = A rs11090305 rs2070470 chr22:24583879 (T/C) 0.2167 176396 0.9706 0.9258 T = T, C = C rs11090305 rs5760244 chr22:24584970 (G/A) 0.2167 177487 0.9706 0.9258 T = G, C = A rs11090305 rs9624413 chr22:24585575 (T/C) 0.2167 178092 0.9706 0.9258 T = T, C = C rs11090305 rs28687166 chr22:24585835 (T/C) 0.2167 178352 0.9706 0.9258 T = T, C = C rs11090305 rs5751813 chr22:24586071 (T/C) 0.2167 178588 0.9706 0.9258 T = T, C = C rs11090305 rs2267066 chr22:24546298 (G/A) 0.2147 138815 0.9763 0.9256 T = G, C = A rs11090305 rs2236623 chr22:24578659 (A/G) 0.2177 171176 0.9649 0.9202 T = A, C = G rs11090305 rs67342915 chr22:24598619 (G/-) 0.2127 191136 0.976 0.9143 T = G, C = - rs11090305 rs4521150 chr22:24600438 (A/G) 0.2177 192955 0.959 0.9091 T = A, C = G rs11090305 rs8138769 chr22:24591879 (A/G) 0.2117 184396 0.9759 0.9087 T = A, C = G rs11090305 rs5760254 chr22:24602382 (A/C) 0.2187 194899 0.9534 0.9037 T = A, C = C rs11090305 . chr22:24417287 (-/G) 0.2286 9804 0.9589 0.8734 T = G, C = - rs62220604 rs6009583 chr22:49677646 (C/T) 0.2465 182 0.989 0.8679 G = C, A = T rs62220604 rs11703376 chr22:49678713 (C/T) 0.2485 1249 0.9781 0.858 G = C, A = T rs62220604 rs8136272 chr22:49678782 (A/T) 0.2515 1318 0.9621 0.8436 G = A, A = T Part B rs2274122 rs679457 chr1:36496479 (A/G) 0.166 −53185 0.9781 0.8679 G = A, A = G rs2274122 rs379507 chr1:36503907 (A/G) 0.166 −45757 0.9781 0.8679 G = A, A = G rs2274122 rs491603 chr1:36532316 (T/C) 0.172 −17348 0.993 0.9333 G = T, A = C rs1984162 rs1984163 chr13:23658864 (A/G) 0.2704 26 1 0.995 A = A, G = G rs8105499 rs8106322 chr19:32024230 (A/G) 0.3519 273 0.995 0.8046 C = A, A = G rs8105499 rs8106852 chr19:32024669 (A/G) 0.3211 712 0.9904 0.9153 C = A, A = G rs8105499 rs8102936 chr19:32027330 (G/A) 0.336 3373 0.9853 0.8467 C = G, A = A rs8105499 rs8103067 chr19:32027415 (G/A) 0.336 3458 0.9853 0.8467 C = G, A = A rs8105499 rs139978707 chr19:32028824 (-/ACAC) 0.332 4867 0.9514 0.8036 C = -, A = ACAC rs11385942 rs35896106 chr3:45841938 (C/T) 0.0855 −34521 0.9595 0.8624 - = C, A = T rs11385942 rs13071258 chr3:45843242 (G/A) 0.0805 −33217 0.9866 0.9733 - = G, A = A rs11385942 rs17763537 chr3:45843315 (C/T) 0.0805 −33144 0.9866 0.9733 - = C, A = T rs11385942 rs34668658 chr3:45844198 (A/C) 0.0815 −32261 1 0.9867 - = A, A = C rs11385942 rs17763742 chr3:45846769 (A/G) 0.0805 −29690 0.9866 0.9733 - = A, A = G rs11385942 rs72893671 chr3:45850783 (T/A) 0.0875 −25676 1 0.9135 - = T, A = A rs11385942 rs17713054 chr3:45859651 (G/A) 0.0805 −16808 1 1 - = G, A = A rs11385942 rs13078854 chr3:45861932 (G/A) 0.0805 −14527 1 1 - = G, A = A rs11385942 rs71325088 chr3:45862952 (T/C) 0.0805 −13507 1 1 - = T, A = C rs11385942 rs10490770 chr3:45864732 (T/C) 0.0805 −11727 1 1 - = T, A = C rs11385942 rs35624553 chr3:45867440 (A/G) 0.0805 −9019 1 1 - = A, A = G rs11385942 rs71619611 chr3:45871139 (A/-) 0.0835 −5320 1 0.9612 - = A, A = - rs11385942 rs67959919 chr3:45871908 (G/A) 0.0805 −4551 1 1 - = G, A = A rs11385942 rs35508621 chr3:45880481 (T/C) 0.0805 4022 1 1 - = T, A = C rs11385942 rs34288077 chr3:45888690 (A/G) 0.0795 12231 1 0.9866 - = A, A = G rs11385942 rs35081325 chr3:45889921 (A/T) 0.0795 13462 1 0.9866 - = A, A = T rs11385942 rs35731912 chr3:45889949 (C/T) 0.0795 13490 1 0.9866 - = C, A = T rs11385942 rs34326463 chr3:45899651 (A/G) 0.0795 23192 1 0.9866 - = A, A = G rs11385942 rs73064425 chr3:45901089 (C/T) 0.0795 24630 1 0.9866 - = C, A = T rs11385942 rs13081482 chr3:45908116 (A/T) 0.0795 31657 1 0.9866 - = A, A = T rs11385942 rs35652899 chr3:45908514 (C/G) 0.0775 32055 1 0.9598 - = C, A = G rs11385942 rs35044562 chr3:45909024 (A/G) 0.0795 32565 1 0.9866 - = A, A = G rs11385942 rs73064431 chr3:45909528 (C/T) 0.0885 33069 0.9865 0.878 - = C, A = T rs11385942 rs13092887 chr3:45909644 (C/A) 0.0865 33185 0.9595 0.8515 - = C, A = A rs11729561 rs11729561 chr4:106943200 (T/C) 0.0736 0 1 1 T = T, C = C rs11729561 rs143299240 chr4:106952273 (-/T) 0.0736 9073 1 1 T = -, C = T rs11729561 rs28472461 chr4:106956065 (C/T) 0.0736 12865 1 1 T = C, C = T rs11729561 rs28709953 chr4:106958075 (C/A) 0.0736 14875 1 1 T = C, C = A rs11729561 rs28663259 chr4:106958076 (C/T) 0.0736 14876 1 1 T = C, C = T rs11729561 rs10023586 chr4:106973602 (A/G) 0.0746 30402 1 0.9856 T = A, C = G rs11729561 rs79449940 chr4:106979613 (G/T) 0.0746 36413 1 0.9856 T = G, C = T rs11729561 rs75853787 chr4:106981645 (C/T) 0.0736 38445 0.9854 0.971 T = C, C = T rs11729561 rs7679603 chr4:106987746 (C/A) 0.0746 44546 1 0.9856 T = C, C = A rs11729561 rs28783132 chr4:106994627 (T/C) 0.0746 51427 1 0.9856 T = T, C = C rs11729561 rs11736679 chr4:106995182 (T/G) 0.0746 51982 1 0.9856 T = T, C = G rs11729561 rs74725815 chr4:106998315 (G/A) 0.0746 55115 1 0.9856 T = G, C = A rs11729561 rs28857517 chr4:107009841 (T/C) 0.0746 66641 1 0.9856 T = T, C = C rs11729561 rs78336797 chr4:107010481 (G/A) 0.0746 67281 1 0.9856 T = G, C = A rs11729561 rs77454815 chr4:107018793 (A/C) 0.0736 75593 0.9854 0.971 T = A, C = C rs11729561 rs28597815 chr4:107020234 (T/C) 0.0736 77034 0.9854 0.971 T = T, C = C rs11729561 rs28823294 chr4:107026693 (C/T) 0.0746 83493 1 0.9856 T = C, C = T rs11729561 rs28786397 chr4:107032979 (T/G) 0.0746 89779 1 0.9856 T = T, C = G rs11729561 rs28648796 chr4:107037155 (G/C) 0.0746 93955 1 0.9856 T = G, C = C rs11729561 rs140517213 chr4:107038130 (C/-) 0.0765 94930 1 0.9579 T = C, C = - rs11729561 rs28786712 chr4:107041195 (A/G) 0.0746 97995 1 0.9856 T = A, C = G rs11729561 rs185825831 chr4:107041846 (C/A) 0.0746 98646 1 0.9856 T = C, C = A rs11729561 rs28890246 chr4:107046012 (C/G) 0.0746 102812 1 0.9856 T = C, C = G rs11729561 rs116513184 chr4:107052399 (A/G) 0.0785 109199 1 0.9317 T = A, C = G rs11729561 rs10010622 chr4:107064347 (A/G) 0.0785 121147 1 0.9317 T = A, C = G rs11729561 rs28843476 chr4:107074493 (G/A) 0.0785 131293 1 0.9317 T = G, C = A rs11729561 rs28848568 chr4:107083274 (C/A) 0.0785 140074 1 0.9317 T = C, C = A rs11729561 rs10010712 chr4:107087041 (G/A) 0.0785 143841 1 0.9317 T = G, C = A rs11729561 rs28852980 chr4:107108590 (T/C) 0.0785 165390 1 0.9317 T = T, C = C rs11729561 rs577231943 chr4:107118572 (A/G) 0.0785 175372 1 0.9317 T = A, C = G rs11729561 rs74349962 chr4:107121289 (T/A) 0.0785 178089 1 0.9317 T = T, C = A rs11729561 rs10026011 chr4:107125358 (A/C) 0.0785 182158 1 0.9317 T = A, C = C rs11729561 rs28668834 chr4:107131465 (G/A) 0.0785 188265 1 0.9317 T = G, C = A rs11729561 rs13258128 chr4:107136176 (G/A) 0.0785 192976 1 0.9317 T = G, C = A rs11729561 rs191250332 chr4:107138855 (A/T) 0.0785 195655 1 0.9317 T = A, C = T rs11729561 rs11729801 chr4:107158823 (C/T) 0.0785 215623 1 0.9317 T = C, C = T rs11729561 rs76805843 chr4:107159508 (A/G) 0.0785 216308 1 0.9317 T = A, C = G rs11729561 rs9996386 chr4:107163773 (T/C) 0.0785 220573 1 0.9317 T = T, C = C rs11729561 rs10033060 chr4:107175191 (C/T) 0.0785 231991 1 0.9317 T = C, C = T rs11729561 rs78279936 chr4:107179034 (T/A) 0.0785 235834 1 0.9317 T = T, C = A rs11729561 rs10213435 chr4:107185588 (A/C) 0.0785 242388 1 0.9317 T = A, C = C rs11729561 rs28432701 chr4:107206834 (G/A) 0.0785 263634 1 0.9317 T = G, C = A rs11729561 rs28600674 chr4:107207836 (T/G) 0.0785 264636 1 0.9317 T = T, C = G rs11729561 rs10009873 chr4:107222264 (C/T) 0.0785 279064 1 0.9317 T = C, C = T rs11729561 rs7356173 chr4:107223219 (T/C) 0.0785 280019 1 0.9317 T = T, C = C rs11729561 rs28408532 chr4:107228745 (G/A) 0.0775 285545 0.9854 0.9172 T = G, C = A rs11729561 rs28722963 chr4:107232881 (T/C) 0.0785 289681 1 0.9317 T = T, C = C rs11729561 rs11544776 chr4:107236833 (C/T) 0.0795 293633 1 0.919 T = C, C = T rs11729561 rs143098221 chr4:107242218 (A/G) 0.0785 299018 0.9853 0.9046 T = A, C = G rs11729561 rs9995260 chr4:107242748 (C/G) 0.0785 299548 0.9853 0.9046 T = C, C = G rs11729561 rs114592099 chr4:107243136 (G/A) 0.0785 299936 0.9853 0.9046 T = G, C = A rs11729561 rs28615207 chr4:107248029 (G/A) 0.0785 304829 0.9853 0.9046 T = G, C = A rs11729561 rs6820647 chr4:107268203 (G/A) 0.0785 325003 0.9853 0.9046 T = G, C = A rs11729561 rs76860372 chr4:107271232 (T/C) 0.0785 328032 0.9853 0.9046 T = T, C = C rs11729561 rs148911649 chr4:107275349 (ATC/-) 0.0785 332149 0.9853 0.9046 T = ATC, C = - rs11729561 rs7682001 chr4:107276380 (G/A) 0.0785 333180 0.9853 0.9046 T = G, C = A rs11729561 rs75431821 chr4:107279518 (T/C) 0.0785 336318 0.9853 0.9046 T = T, C = C rs11729561 rs146578076 chr4:107288993 (-/AAGT) 0.0775 345793 0.9707 0.8901 T = -, C = AAGT rs657152 rs8176719 chr9:136132908 (-/C) 0.3946 −6357 0.9958 0.9713 C = -, A = C rs657152 rs687621 chr9:136137065 (A/G) 0.3708 −2200 0.9955 0.8775 C = A, A = G rs657152 rs687289 chr9:136137106 (G/A) 0.3718 −2159 0.9955 0.8812 C = G, A = A rs657152 rs576123 chr9:136144308 (T/C) 0.3698 5043 0.9955 0.8737 C = T, A = C rs657152 rs61457395 chr9:136145907 (-/A) 0.3708 6642 1 0.8854 C = -, A = A rs657152 rs367689313 chr9:136145993 (AGAAGGGAAATTAATAAATATT/-) 0.3698 6728 1 0.8816 C = AGAAGGGAAATTAATAAATATT, A = - rs657152 rs8176645 chr9:136149098 (T/A) 0.3966 9833 0.9876 C = T, A = A Part C rs115492982 rs7543314 chr1:150271247 (G/A) 0.002 −309 1 1 G = G, A = A rs115492982 rs3738322 chr1:150272038 (G/A) 0.002 482 1 1 G = G, A = A rs115492982 rs16835865 chr1:150270667 (C/T) 0.002 −889 1 1 G = C, A = T rs115492982 rs73013119 chr1:150272447 (A/G) 0.002 891 1 1 G = A, A = G rs115492982 rs112587175 chr1:150273621 (C/A) 0.002 2065 1 1 G = C, A = A rs115492982 rs56965166 chr1:150269187 (A/T) 0.002 −2369 1 1 G = A, A = T rs115492982 rs16830437 chr1:150266903 (T/C) 0.002 −4653 1 1 G = T, A = C rs115492982 rs16835791 chr1:150265839 (A/C) 0.002 −5717 1 1 G = A, A = C rs115492982 rs143549387 chr1:150277642 (A/-) 0.002 6086 1 1 G = A, A = - rs115492982 rs16835782 chr1:150265360 (A/G) 0.002 −6196 1 1 G = A, A = G rs115492982 rs369956581 chr1:150264667 (1/-) 0.002 −6889 1 1 G = T, A = - rs115492982 rs73011400 chr1:150264215 (A/G) 0.002 −7341 1 1 G = A, A = G rs115492982 rs16835911 chr1:150279333 (A/C) 0.002 7777 1 1 G = A, A = C rs115492982 rs57361164 chr1:150261893 (A/G) 0.002 −9663 1 1 G = A, A = G rs115492982 rs73013129 chr1:150281404 (T/C) 0.002 9848 1 1 G = T, A = C rs115492982 rs112097040 chr1:150283854 (C/T) 0.002 12298 1 1 G = C, A = T rs115492982 rs146795912 chr1:150285359 (G/A) 0.002 13803 1 1 G = G, A = A rs115492982 rs113720135 chr1:150285600 (G/A) 0.002 14044 1 1 G = G, A = A rs115492982 rs60531845 chr1:150285818 (C/T) 0.002 14262 1 1 G = C, A = T rs115492982 rs3054393 chr1:150256412 (-/TTTATT) 0.002 −15144 1 1 G = -, A = TTTATT rs115492982 rs16835708 chr1:150253872 (C/G) 0.002 −17684 1 1 G = C, A = G rs115492982 rs112511224 chr1:150289455 (C/A) 0.002 17899 1 1 G = C, A = A rs115492982 rs142046449 chr1:150290133 (C/T) 0.002 18577 1 1 G = C, A = T rs115492982 rs16835699 chr1:150252247 (T/C) 0.002 −19309 1 1 G = T, A = C rs115492982 rs58516261 chr1:150290969 (C/T) 0.002 19413 1 1 G = C, A = T rs115492982 rs74953512 chr1:150251816 (T/A) 0.002 −19740 1 1 G = T, A = A rs115492982 rs79484682 chr1:150292342 (C/T) 0.002 20786 1 1 G = C, A = T rs115492982 rs73015063 chr1:150293416 (A/G) 0.002 21860 1 1 G = A, A = G rs115492982 rs113579391 chr1:150247103 (C/T) 0.002 −24453 1 1 G = C, A = T rs115492982 rs73011384 chr1:150246411 (C/T) 0.002 −25145 1 1 G = C, A = T rs115492982 rs60758881 chr1:150297241 (T/C) 0.002 25685 1 1 G = T, A = C rs115492982 rs114657335 chr1:150298649 (C/G) 0.002 27093 1 1 G = C, A = G rs115492982 rs587687867 chr1:150244075 (G/A) 0.002 −27481 1 1 G = G, A = A rs115492982 rs111644778 chr1:150243179 (C/T) 0.002 −28377 1 1 G = C, A = T rs115492982 rs2275779 chr1:150300507 (A/G) 0.002 28951 1 1 G = A, A = G rs115492982 rs4926420 chr1:150303244 (T/C) 0.002 31688 1 1 G = T, A = C rs115492982 rs112431552 chr1:150303670 (A/G) 0.002 32114 1 1 G = A, A = G rs115492982 rs112265199 chr1:150303734 (C/G) 0.002 32178 1 1 G = C, A = G rs115492982 rs6700607 chr1:150304107 (TG) 0.002 32551 1 1 G = T, A = G rs115492982 rs587595457 chr1:150237837 (T/C) 0.002 −33719 1 1 G = T, A = C rs115492982 rs80215841 chr1:150306471 (A/G) 0.002 34915 1 1 G = A, A = G rs115492982 rs60456922 chr1:150236472 (C/T) 0.002 −35084 1 1 G = C, A = T rs115492982 rs6679726 chr1:150308908 (G/A) 0.002 37352 1 1 G = G, A = A rs115492982 rs58373639 chr1:150309640 (A/T) 0.002 38084 1 1 G = A, A = T rs115492982 rs6700009 chr1:150310159 (A/C) 0.002 38603 1 1 G = A, A = C rs115492982 rs73015081 chr1:150313761 (T/C) 0.002 42205 1 1 G = T, A = C rs115492982 rs373322282 chr1:150227390 (ATGGA/-) 0.002 −44166 1 1 G = ATGGA, A = - rs115492982 rs16836130 chr1:150316265 (A/C) 0.002 44709 1 1 G = A, A = C rs115492982 rs16836139 chr1:150318369 (G/A) 0.002 46813 1 1 G = G, A = A rs115492982 rs111863435 chr1:150223285 (C/T) 0.002 −48271 1 1 G = C, A = T rs115492982 rs57163995 chr1:150320622 (G/A) 0.002 49066 1 1 G = G, A = A rs115492982 rs3737319 chr1:150321798 (T/G) 0.002 50242 1 1 G = T, A = G rs115492982 rs111334066 chr1:150219183 (C/A) 0.002 −52373 1 1 G = C, A = A rs115492982 rs116262820 chr1:150218023 (C/T) 0.002 −53533 1 1 G = C, A = T rs115492982 rs587674887 chr1:150215634 (A/C) 0.002 −55922 1 1 G = A, A = C rs115492982 rs2015955 chr1:150327788 (C/T) 0.002 56232 1 1 G = C, A = T rs115492982 rs2015966 chr1:150327847 (G/A) 0.002 56291 1 1 G = G, A = A rs115492982 rs111275178 chr1:150213525 (G/A) 0.002 −58031 1 1 G = G, A = A rs115492982 rs73015095 chr1:150329821 (G/T) 0.002 58265 1 1 G = G, A = T rs115492982 rs200378817 chr1:150329986 (-/TT) 0.002 58430 1 1 G = -, A = TT rs115492982 rs73015096 chr1:150331437 (G/A) 0.002 59881 1 1 G = G, A = A rs115492982 rs112896715 chr1:150333692 (G/A) 0.002 62136 1 1 G = G, A = A rs115492982 rs16836442 chr1:150338613 (T/C) 0.002 67057 1 1 G = T, A = C rs115492982 rs149553874 chr1:150341202 (A/G) 0.002 69646 1 1 G = A, A = G rs115492982 rs73017006 chr1:150341765 (G/A) 0.002 70209 1 1 G = G, A = A rs115492982 rs3850843 chr1:150348335 (A/G) 0.002 76779 1 1 G = A, A = G rs115492982 rs148424403 chr1:150349418 (G/A) 0.002 77862 1 1 G = G, A = A rs115492982 rs16836576 chr1:150351001 (T/C) 0.002 79445 1 1 G = T, A = C rs115492982 rs73017073 chr1:150353003 (T/C) 0.002 81447 1 1 G = T, A = C rs115492982 rs16836594 chr1:150354584 (T/C) 0.002 83028 1 1 G = T, A = C rs115492982 rs16836601 chr1:150356343 (T/C) 0.002 84787 1 1 G = T, A = C rs115492982 rs60459288 chr1:150356425 (A/G) 0.002 84869 1 1 G = A, A = G rs115492982 rs145792768 chr1:150356925 (C/-) 0.002 85369 1 1 G = C, A = - rs115492982 rs200485038 chr1:150360844 (TATACACA/-) 0.002 89288 1 1 G = TATACACA, A = - rs115492982 rs145326563 chr1:150176573 (G/A) 0.002 −94983 1 1 G = G, A = A rs115492982 rs59367061 chr1:150368788 (C/T) 0.002 97232 1 1 G = C, A = T rs115492982 rs75909586 chr1:150174286 (T/C) 0.002 −97270 1 1 G = T, A = C rs115492982 rs56909494 chr1:150368879 (G/T) 0.002 97323 1 1 G = G, A = T rs115492982 rs56882505 chr1:150371505 (G/T) 0.002 99949 1 1 G = G, A = T rs115492982 rs76711752 chr1:150371599 (T/C) 0.002 100043 1 1 G = T, A = C rs115492982 rs112294023 chr1:150374142 (C/T) 0.002 102586 1 1 G = C, A = T rs115492982 rs59639798 chr1:150374712 (G/A) 0.002 103156 1 1 G = G, A = A rs115492982 rs146199040 chr1:150167946 (C/T) 0.002 −103610 1 1 G = C, A = T rs115492982 rs73017086 chr1:150376628 (T/C) 0.002 105072 1 1 G = T, A = C rs115492982 rs587746671 chr1:150166093 (G/A) 0.002 −105463 1 1 G = G, A = A rs115492982 rs76176241 chr1:150379851 (G/T) 0.002 108295 1 1 G = G, A = T rs115492982 rs16836786 chr1:150380364 (C/G) 0.002 108808 1 1 G = C, A = G rs115492982 rs111982037 chr1:150380739 (C/G) 0.002 109183 1 1 G = C, A = G rs115492982 rs80029546 chr1:150160082 (A/G) 0.002 −111474 1 1 G = A, A = G rs115492982 rs145119256 chr1:150383919 (A/G) 0.002 112363 1 1 G = A, A = G rs115492982 rs73017092 chr1:150384576 (G/T) 0.002 113020 1 1 G = G, A = T rs115492982 rs4926430 chr1:150385500 (G/A) 0.002 113944 1 1 G = G, A = A rs115492982 rs144073030 chr1:150385864 (G/A) 0.002 114308 1 1 G = G, A = A rs115492982 rs6688983 chr1:150157150 (C/T) 0.002 −114406 1 1 G = C, A = T rs115492982 rs111802234 chr1:150386950 (G/-) 0.002 115394 1 1 G = G, A = - rs115492982 rs115006285 chr1:150155187 (C/T) 0.002 −116369 1 1 G = C, A = T rs115492982 rs370281030 chr1:150388745 (TGA/-) 0.002 117189 1 1 G = TGA, A = - rs115492982 rs59378360 chr1:150391946 (A/G) 0.002 120390 1 1 G = A, A = G rs115492982 rs112490454 chr1:150392392 (A/G) 0.002 120836 1 1 G = A, A = G rs115492982 rs80012313 chr1:150147374 (T/A) 0.002 −124182 1 1 G = T, A = A rs115492982 rs113371939 chr1:150396417 (C/G) 0.002 124861 1 1 G = C, A = G rs115492982 rs147729724 chr1:150397877 (T/C) 0.002 126321 1 1 G = T, A = C rs115492982 rs59662772 chr1:150398202 (G/A) 0.002 126646 1 1 G = G, A = A rs115492982 rs7550339 chr1:150140661 (C/T) 0.002 −130895 1 1 G = T, A = C rs115492982 rs77778882 chr1:150140581 (A/G) 0.002 −130975 1 1 G = A, A = G rs115492982 rs10788870 chr1:150140540 (A/C) 0.002 −131016 1 1 G = C, A = A rs115492982 rs1382572 chr1:150139471 (T/C) 0.002 −132085 1 1 G = C, A = T rs115492982 rs73017102 chr1:150403756 (G/A) 0.002 132200 1 1 G = G, A = A rs115492982 rs112235324 chr1:150404205 (T/C) 0.002 132649 1 1 G = T, A = C rs115492982 rs73020860 chr1:150134895 (G/A) 0.002 −136661 1 1 G = G, A = A rs115492982 rs6685607 chr1:150134341 (G/A) 0.002 −137215 1 1 G = G, A = A rs115492982 rs111400442 chr1:150410258 (G/A) 0.002 138702 1 1 G = G, A = A rs115492982 rs113274217 chr1:150410365 (C/T) 0.002 138809 1 1 G = C, A = T rs115492982 rs60527237 chr1:150131893 (C/T) 0.002 −139663 1 1 G = C, A = T rs115492982 rs57971032 chr1:150411292 (G/A) 0.002 139736 1 1 G = G, A = A rs115492982 rs113408614 chr1:150411507 (C/T) 0.002 139951 1 1 G = C, A = T rs115492982 rs6681679 chr1:150130526 (C/T) 0.002 −141030 1 1 G = C, A = T rs115492982 rs149608182 chr1:150416426 (G/A) 0.002 144870 1 1 G = G, A = A rs115492982 rs149443445 chr1:150126326 (-/CT) 0.002 −145230 1 1 G = -, A = CT rs115492982 rs16836943 chr1:150418796 (C/T) 0.002 147240 1 1 G = C, A = T rs115492982 rs112272272 chr1:150419395 (T/C) 0.002 147839 1 1 G = T, A = C rs115492982 rs73019017 chr1:150421654 (T/C) 0.002 150098 1 1 G = T, A = C rs115492982 rs73019018 chr1:150421965 (T/C) 0.002 150409 1 1 G = T, A = C rs115492982 rs73019020 chr1:150422271 (G/A) 0.002 150715 1 1 G = G, A = A rs115492982 rs73019021 chr1:150422548 (T/A) 0.002 150992 1 1 G = T, A = A rs115492982 rs6680391 chr1:150424182 (A/G) 0.002 152626 1 1 G = A, A = G rs115492982 rs7532297 chr1:150117691 (A/G) 0.002 −153865 1 1 G = A, A = G rs115492982 rs77553465 chr1:150427507 (G/A) 0.002 155951 1 1 G = G, A = A rs115492982 rs7517537 chr1:150114083 (C/T) 0.002 −157473 1 1 G = C, A = T rs115492982 rs371028395 chr1:150430107 (A/-) 0.002 158551 1 1 G = A, A = - rs115492982 rs73019027 chr1:150430552 (C/T) 0.002 158996 1 1 G = C, A = T rs115492982 rs113104968 chr1:150109281 (C/T) 0.002 −162275 1 1 G = C, A = T rs115492982 rs12090508 chr1:150107793 (A/G) 0.002 −163763 1 1 G = A, A = G rs115492982 rs57272513 chr1:150436496 (A/G) 0.002 164940 1 1 G = A, A = G rs115492982 rs73019028 chr1:150437283 (G/C) 0.002 165727 1 1 G = G, A = C rs115492982 rs111536367 chr1:150438467 (C/A) 0.002 166911 1 1 G = C, A = A rs115492982 rs35766167 chr1:150103818 (1/-) 0.002 −167738 1 1 G = -, A = T rs115492982 rs112640811 chr1:150097784 (G/A) 0.002 −173772 1 1 G = G, A = A rs115492982 rs12082615 chr1:150097384 (A/C) 0.002 −174172 1 1 G = A, A = C rs115492982 rs7530672 chr1:150096444 (G/A) 0.002 −175112 1 1 G = G, A = A rs115492982 rs13057 chr1:150448688 (G/T) 0.002 177132 1 1 G = G, A = T rs115492982 rs6677707 chr1:150092918 (A/G) 0.002 −178638 1 1 G = A, A = G rs115492982 rs7533714 chr1:150092086 (T/C) 0.002 −179470 1 1 G = C, A = T rs115492982 rs9727702 chr1:150090669 (G/A) 0.002 −180887 1 1 G = G, A = A rs115492982 rs11205328 chr1:150087865 (T/C) 0.002 −183691 1 1 G = T, A = C rs115492982 rs113887124 chr1:150456665 (G/A) 0.002 185109 1 1 G = G, A = A rs115492982 rs3840448 chr1:150459893 (TGTT/-) 0.002 188337 1 1 G = TGIT, A = - rs115492982 rs2275245 chr1:150460348 (C/T) 0.002 188792 1 1 G = C, A = T rs115492982 rs3839012 chr1:150461761 (C/-) 0.002 190205 1 1 G = C, A = - rs115492982 rs871527 chr1:150462088 (C/G) 0.002 190532 1 1 G = C, A = G rs115492982 rs10624875 chr1:150463530 (-/AA) 0.002 191974 1 1 G = -, A = AA rs115492982 rs142587704 chr1:150078791 (dc!-) 0.002 −192765 1 1 G = CTC, A = - rs115492982 rs111842933 chr1:150465271 (G/A) 0.002 193715 1 1 G = G, A = A rs115492982 rs143706301 chr1:150467213 (-/A) 0.002 195657 1 1 G = -, A = A rs115492982 rs953127 chr1:150469256 (G/T) 0.002 197700 1 1 G = G, A = T rs115492982 rs112820016 chr1:150071489 (C/T) 0.002 −200067 1 1 G = C, A = T rs115492982 rs587637589 chr1:150476115 (TA/-) 0.002 204559 1 1 G = TA, A = - rs115492982 rs28541919 chr1:150476117 (A/G) 0.002 204561 1 1 G = A, A = G rs115492982 rs12058524 chr1:150066384 (C/T) 0.002 −205172 1 1 G = C, A = T rs115492982 rs3834087 chr1:150478538 (GAG/-) 0.002 206982 1 1 G = GAG, A = - rs115492982 rs111414303 chr1:150063936 (G/A) 0.002 −207620 1 1 G = G, A = A rs115492982 rs73019054 chr1:150485599 (G/A) 0.002 214043 1 1 G = G, A = A rs115492982 rs79595845 chr1:150055388 (T/A) 0.002 −216168 1 1 G = T, A = A rs115492982 rs78312541 chr1:150487797 (C/A) 0.002 216241 1 1 G = C, A = A rs115492982 rs58419446 chr1:150490370 (C/T) 0.002 218814 1 1 G = C, A = T rs115492982 rs113872537 chr1:150493557 (G/A) 0.002 222001 1 1 G = G, A = A rs115492982 rs79524321 chr1:150495269 (T/C) 0.002 223713 1 1 G = T, A = C rs115492982 rs11205325 chr1:150044324 (G/A) 0.002 −227232 1 1 G = G, A = A rs115492982 rs11205324 chr1:150041872 (G/T) 0.002 −229684 1 1 G = T, A = G rs115492982 rs11205323 chr1:150041871 (C/A) 0.002 −229685 1 1 G = A, A = C rs115492982 rs147106269 chr1:150502105 (G/A) 0.002 230549 1 1 G = G, A = A rs115492982 rs9887866 chr1:150039267 (T/C) 0.002 −232289 1 1 G = C, A = T rs115492982 rs77173601 chr1:150505070 (G/A) 0.002 233514 1 1 G = G, A = A rs115492982 rs6657478 chr1:150507567 (A/G) 0.002 236011 1 1 G = A, A = G rs115492982 rs78006356 chr1:150032621 (C/T) 0.002 −238935 1 1 G = C, A = T rs115492982 rs113085079 chr1:150512512 (G/T) 0.002 240956 1 1 G = G, A = T rs115492982 rs6687257 chr1:150029702 (T/C) 0.002 −241854 1 1 G = T, A = C rs115492982 rs139447592 chr1:150513837 (G/T) 0.002 242281 1 1 G = G, A = T rs115492982 rs145793287 chr1:150514145 (C/T) 0.002 242589 1 1 G = C, A = T rs115492982 rs7514515 chr1:150517312 (G/C) 0.002 245756 1 1 G = G, A = C rs115492982 rs75513680 chr1:150021709 (G/A) 0.002 −249847 1 1 G = G, A = A rs115492982 rs57507911 chr1:150523982 (AG/-) 0.002 252426 1 1 G = AG, A = - rs115492982 rs61684558 chr1:150524277 (G/A) 0.002 252721 1 1 G = G, A = A rs115492982 rs144832337 chr1:150531320 (G/A) 0.002 259764 1 1 G = G, A = A rs115492982 rs140930998 chr1:150011383 (G/A) 0.002 −260173 1 1 G = G, A = A rs115492982 rs147310031 chr1:150011375 (G/A) 0.002 −260181 1 1 G = G, A = A rs115492982 rs116614291 chr1:150531959 (G/A) 0.002 260403 1 1 G = G, A = A rs115492982 rs79568347 chr1:150006818 (T/C) 0.002 −264738 1 1 G = T, A = C rs115492982 rs145686348 chr1:150558152 (G/T) 0.002 286596 1 1 G = G, A = T rs115492982 rs142892208 chr1:150560164 (A/-) 0.002 288608 1 1 G = A, A = - rs115492982 rs6691535 chr1:150568894 (A/G) 0.002 297338 1 1 G = A, A = G rs115492982 rs143004068 chr1:150582871 (G/A) 0.002 311315 1 1 G = G, A = A rs115492982 rs74124941 chr1:150588009 (G/A) 0.002 316453 1 1 G = G, A = A rs115492982 rs587647594 chr1:150601298 (G/T) 0.002 329742 1 1 G = G, A = T rs115492982 rs74124944 chr1:150603752 (T/A) 0.002 332196 1 1 G = T, A = A rs115492982 rs75508758 chr1:150604410 (A/G) 0.002 332854 1 1 G = A, A = G rs115492982 rs145393663 chr1:150605772 (C/T) 0.002 334216 1 1 G = C, A = T rs115492982 rs57025631 chr1:150607284 (G/T) 0.002 335728 1 1 G = G, A = T rs115492982 rs74124966 chr1:150609082 (G/A) 0.002 337526 1 1 G = G, A = A rs115492982 rs139726900 chr1:150612476 (G/A) 0.002 340920 1 1 G = G, A = A rs115492982 rs1877469 chr1:150619602 (T/C) 0.002 348046 1 1 G = C, A = T rs115492982 rs1151917 chr1:150622696 (T/A) 0.002 351140 1 1 G = T, A = A rs115492982 rs1241578 chr1:150628365 (A/C) 0.002 356809 1 1 G = A, A = C rs115492982 rs1241579 chr1:150630869 (T/C) 0.002 359313 1 1 G = T, A = C rs115492982 rs1707158 chr1:150633833 (A/G) 0.002 362277 1 1 G = A, A = G rs115492982 rs2458393 chr1:150634596 (G/A) 0.002 363040 1 1 G = G, A = A rs115492982 rs1241575 chr1:150646372 (A/T) 0.002 374816 1 1 G = A, A = T rs115492982 rs73008805 chr1:150676968 (T/C) 0.002 405412 1 1 G = T, A = C rs115492982 rs57127659 chr1:150684117 (C/T) 0.002 412561 1 1 G = C, A = T rs115492982 rs73008807 chr1:150687686 (G/T) 0.002 416130 1 1 G = G, A = T rs115492982 rs587594230 chr1:150700505 (G/A) 0.002 428949 1 1 G = G, A = A rs115492982 rs192267029 chr1:150702269 (G/C) 0.002 430713 1 1 G = G, A = C rs115492982 rs113739463 chr1:150714363 (G/C) 0.002 442807 1 1 G = G, A = C rs115492982 rs73008818 chr1:150718179 (G/A) 0.002 446623 1 1 G = G, A = A rs115492982 rs112325265 chr1:150727987 (G/A) 0.002 456431 1 1 G = G, A = A rs115492982 rs113422136 chr1:150733416 (G/A) 0.002 461860 1 1 G = G, A = A rs115492982 rs28675769 chr1:150736807 (C/T) 0.002 465251 1 1 G = C, A = T rs115492982 rs112049924 chr1:150748342 (T/C) 0.002 476786 1 1 G = T, A = C rs115492982 rs112037529 chr1:150757290 (G/C) 0.002 485734 1 1 G = G, A = C

In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 polymorphisms associated with a severe response to a Coronavirus infection are selected from the polymorphisms provided Table 1 and Table 6a or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 polymorphisms or at least 306 associated with a severe response to a Coronavirus infection are selected from the polymorphisms provided Table 1 or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 polymorphisms associated with a severe response to a Coronavirus infection are selected from the polymorphisms provided Table 2 and Table 6a or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50 polymorphisms associated with a severe response to a Coronavirus infection are selected from polymorphisms provided in Table 2 or Table 6a or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50 polymorphisms associated with a severe response to a Coronavirus infection are selected from polymorphisms provided in Table 2 or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50 polymorphisms associated with a severe response to a Coronavirus infection are selected from polymorphisms provided in Table 3 or a polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50 or at least 60 polymorphisms associated with a severe response to a Coronavirus infection are selected from polymorphisms provided in Table 4 or a polymorphism in linkage disequilibrium with one or more thereof.

In embodiment, the method of the invention involves detecting the presence of each of the polymorphisms provided in Table 2 or a polymorphism in linkage disequilibrium with one or more thereof.

In embodiment, the method of the invention involves detecting the presence of each of the polymorphisms provided in Table 3 or a polymorphism in linkage disequilibrium with one or more thereof.

In embodiment, the method of the invention involves detecting the presence of each of the polymorphisms provided in Table 4 or a polymorphism in linkage disequilibrium with one or more thereof.

In embodiment, the method of the invention involves detecting the presence of each of the polymorphisms provided in Table 19 or a polymorphism in linkage disequilibrium with one or more thereof.

In embodiment, the method of the invention involves detecting the presence of each of the polymorphisms provided in Table 22 or a polymorphism in linkage disequilibrium with one or more thereof.

Polymorphisms in linkage disequilibrium with those specifically mentioned herein are easily identified by those of skill in the art. Table 6a provides examples of linked loci for the polymorphisms listed in Table 3. Table 6b provides examples of linked loci for the polymorphisms listed in Table 4 which are not provided in Table 6a. Such linked polymorphisms for the other polymorphisms listed in Table 1 can very easily be identified by the skilled person using the HAPMAP database.

Where relevant in each Table, the A1 or Allele 1 is the risk (minor allele) associated allele. The risk allele may be associated with a decreased or increased risk as described herein. As used herein, the terms “A1” and “Allele 1” are used interchangeably. As used herein, the terms “A2” and “Allele 2” are used interchangeably.

In an embodiment, if the method includes the analysis of rs11385942 and/or rs657152 the method further comprises detecting at least one other polymorphism provided in any one of Tables 1 to 6, 8, 19 or 22, or a polymorphism in linkage disequilibrium therewith.

Calculating Composite Relative Risk “Genetic Risk”

An individual's “genetic risk” can be defined as the product of genotype relative risk values for each polymorphism assessed. A log-additive risk model can then be used to define three genotypes AA, AB and BB for a polymorphism having relative risk values of 1, OR, and OR², under a rare disease model, where OR is the previously reported disease odds ratio for the high-risk allele, B, vs the low-risk allele, A. If the B allele has frequency (p), then these genotypes have population frequencies of (1−p)², 2p(1−p), and p², assuming Hardy-Weinberg equilibrium. The genotype relative risk values for each polymorphism can then be scaled so that based on these frequencies the average relative risk in the population is 1. Specifically, given the unscaled population average relative risk for each SNP:

(μ)=(1−p)²+2p(1−p)OR+p ²OR²

Adjusted risk values 1/μ, OR/μ, and OR²/μ are used for AA, AB, and BB genotypes for each SNP. Missing genotypes are assigned a relative risk of 1. The following formula can be used to define the genetic risk:

SNP₁×SNP₂×SNP₃×SNP₄×SNP₅×SNP₆×SNP₇,×SNP₈, etc.

Similar calculations can be performed for non-SNP polymorphisms or a combination thereof.

An alternate method for calculating the composite risk is described in Mavaddat et al. (2015). In this example, the following formula is used;

PRS=β₁ x ₁+β₂ x ₂+ . . . β_(κ) x _(κ)+β_(n) x _(n)

where β_(κ) is the per-allele log odds ratio (OR) for the minor allele for SNP κ, and x_(κ) the number of alleles for the same SNP (0, 1 or 2), n is the total number of SNPs and PRS is the polygenic risk score (which can also be referred to as composite SNP risk). Similar calculations can be performed for non-SNP polymorphisms or a combination thereof.

In an alternate embodiment, the magnitude of effect of each risk allele is not used when calculating the genetic risk score. More specifically, allele counting as generally described in WO 2005/086770 is used. For example, in one embodiment if the subject was homozygous for the risk allele they were scored as 2, if they were heterozygous for the risk allele they were scored as 1, and if they were homozygous for the risk allele they were scored as 0. As the skilled person would appreciate, alternate values such as 1, 0.5 and 0 respectively, could be used.

In an embodiment, the percent of risk alleles present out of the total possible number of loci analysed is used to produce the genetic risk score. For example, in the 64 allele panel described in Example 5 the subject may have at most 128 risk alleles. If a subject had 64 out of these 128 alleles, they would have 50% of the total possible alleles which can be expressed as 0.5.

The genetic risk score can be expressed as:

ln_risk=−8.4953 (i.e. the model intercept)+0.1496×SNP %. Then, risk=exp(ln_risk).

In this example, the risk is the relative risk for severe disease (e.g. a person with risk=3.5 is at 3.5 times increased risk compared with a person with the average number of risk alleles). exp(β) is the odds ratio for an increase of 1% in risk alleles. So, exp(0.1496)=1.16, which means that risk increases by 16% for a 1% increase in SNP %. In an embodiment, the β coefficient (model intercept) is between −10.06391 to −6.926615, or −9.5 to −7.5, or −9 to −8. In an embodiment of the above formula, the adjustment of the starting ln(risk) for the percentage of risk alleles is 0.1237336 to 0.1755347, or 0.16 to 0.14.

In an embodiment, the genetic risk is the SNP Risk Factor (SNF). In one embodiment, SNF=Σ(No of Risk Alleles×SNP β coefficient).

The “risk” of a human subject developing a severe response to a Coronavirus infection can be provided as a relative risk (or risk ratio).

In an embodiment, the genetic risk assessment obtains the “relative risk” of a human subject developing a severe response to a Coronavirus infection. Relative risk (or risk ratio), measured as the incidence of a disease in individuals with a particular characteristic (or exposure) divided by the incidence of the disease in individuals without the characteristic, indicates whether that particular exposure increases or decreases risk. Relative risk is helpful to identify characteristics that are associated with a disease, but by itself is not particularly helpful in guiding screening decisions because the frequency of the risk (incidence) is cancelled out.

In an embodiment, a threshold value(s) is set for determining a particular action such as the need for routine diagnostic testing, the need for prophylactic anti-Coronavirus therapy, selection of a person for a vaccine or the need to administer an anti-Coronavirus therapy. For example, a score determined using a method of the invention is compared to a pre-determined threshold, and if the score is higher than the threshold a recommendation is made to take the pre-determined action. Methods of setting such thresholds have now become widely used in the art and are described in, for example, US 20140018258.

Clinical Risk Assessment

In an embodiment, the method further comprises performing a clinical risk assessment of the human subject; and combining the clinical risk assessment and the genetic risk assessment to obtain the risk of a human subject developing a severe response to a Coronavirus infection. The clinical risk assessment procedure can include obtaining clinical information from a human subject. In other embodiments, these details have already been determined (such as in the subject's medical records).

Examples of factors which can be used to produce the clinical risk assessment include, but are not limited to, obtaining information from the human on one or more of the following: age, family history of a severe response to a Coronavirus infection, race/ethnicity, gender, body mass index, total cholesterol level, systolic and/or diastolic blood pressure, smoking status, does the human have diabetes, does the human have a cardiovascular disease, is the subject on hypertension medication, loss of taste, loss of smell and white blood cell count.

In an embodiment, the clinical risk assessment is based only one or more or all of age, body mass index, loss of taste, loss of smell and smoking status.

In another embodiment, the clinical risk assessment is based only one or more or all of age, loss of taste, loss of smell and smoking status.

In an embodiment, the clinical risk assessment includes obtaining information from the subject on one or more or all of age, gender, race/ethnicity, blood type, does the human have or has had an autoimmune disease, does the human have or has had an haematological cancer, does the human have or has had an non-haematological cancer, does the human have or has had diabetes, does the human have or has had hypertension and does the human have or has had a respiratory disease (other than asthma).

In an embodiment, the clinical risk assessment at least includes age and gender.

The present inventors have also found that a severe response to a Coronavirus infection risk model that relies solely on clinical factors provides useful risk discrimination for assessing a subject's risk of developing a severe response to a Coronavirus infection such as a SARS-CoV-2 infection. Such a test may be particularly useful in circumstances where a rapid decision needs to be made and/or when genetic testing is not readily available. Thus, in another aspect the present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising performing a clinical risk assessment of the human subject, wherein the clinical risk assessment comprises obtaining information from the subject on two, three, four, five or more or all of age, gender, race/ethnicity, height, weight, blood type, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had an autoimmune disease, does the human have or has had an haematological cancer, does the human have or has had an immunocompromised disease, does the human have or has had an non-haematological cancer, does the human have or has had diabetes, does the human have or has had liver disease, does the human have or has had hypertension and does the human have or has had a respiratory disease (other than asthma).

In an embodiment, the method comprises obtaining information from the subject on age and gender.

In an embodiment, the method comprises obtaining information from the subject on age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).

In an embodiment, the method comprises obtaining information from the subject on age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).

Examples of respiratory diseases which are included in the test are chronic obstructive pulmonary disease, chronic bronchitis and emphysema.

The diabetes can be any type of diabetes.

In an embodiment, the clinical risk assessment is conducted using the following formula:

$\begin{matrix} {\ln\left( {risk} \right)} & {= {{Model}\mspace{14mu}{Intercept}}} \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{clinical}\mspace{14mu}{factor}\mspace{14mu}{one}\mspace{14mu}{applies}} \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{clinical}\mspace{14mu}{factor}\mspace{14mu}{two}\mspace{14mu}{applies}} \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{clinical}\mspace{14mu}{factor}\mspace{14mu}{three}\mspace{14mu}{applies}} \\ \ldots & \; \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{clinical}\mspace{14mu}{factor}\mspace{14mu} n\mspace{14mu}{applies}} \end{matrix}$

Where OR=Odds Ratio.

In an embodiment, the clinical risk assessment is conducted using the following formula:

$\begin{matrix} {\ln\left( {risk} \right)} & {= {{Model}\mspace{14mu}{Intercept}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {18\text{-}29\mspace{14mu}{years}\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {30\text{-}39\mspace{14mu}{years}\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {40\text{-}49\mspace{14mu}{years}\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {60\text{-}69\mspace{14mu}{years}\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {70\text{+}\mspace{14mu}{year}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{gender}} = {male}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{ethnicity}} = {{non}\text{-Caucasian}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = {A\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = {B\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = {A\; B}} \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{autoimmune}\mspace{14mu}{disease}\mspace{20mu}\left( {{namely},} \right.} \\ \; & {\left. {{{rheumatoid}\mspace{14mu}{arthritis}},{lupus},{{or}\mspace{14mu}{psoriasis}}} \right) = {yes}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{cancer}},{{haematological} = {yes}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{cancer}},{{{non}\text{-}{haematological}} = {yes}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{diabetes}} = {yes}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{hypertension}} = {yes}} \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{repiratory}\mspace{14mu}{disease}\mspace{14mu}\left( {{other}\mspace{14mu}{than}} \right.} \\ \; & {\left. {asthma} \right) = {yes}} \end{matrix}$

Where OR=Odds Ratio.

Using the above formulae the relative risk of a human subject developing a severe response to a Coronavirus infection is: risk=

.

In one example, the clinical risk assessment is conducted using the following formula:

$\begin{matrix} {\ln\left( {risk} \right)} & = & {- 0.2645} & \; \\ \; & + & {- 1.3111} & {{{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {18\text{-}29\mspace{14mu}{years}}} \\ \; & + & {- 0.8348} & {{{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {30\text{-}39\mspace{14mu}{years}}} \\ \; & + & {- 0.4038} & {{{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {40\text{-}49\mspace{14mu}{years}}} \\ \; & + & {- 0.0973} & {{{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {60\text{-}69\mspace{14mu}{years}}} \\ \; & + & 0.4419 & {{{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {70\text{+}\mspace{14mu}{year}}} \\ \; & + & 0.0855 & {{{if}\mspace{14mu}{gender}} = {male}} \\ \; & + & 0.0404 & {{{if}\mspace{14mu}{ethnicity}} = {{non}\text{-Caucasian}}} \\ \; & + & {- 0.0614} & {{{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = A} \\ \; & + & 0.2039 & {{{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = B} \\ \; & + & {- 0.5541} & {{{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = {A\; B}} \\ \; & + & 0.5424 & {{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{autoimmune}\mspace{14mu}{disease}} \\ \; & \; & \; & \left( {{namely},{{rheumatoid}\mspace{14mu}{arthritis}},{lupus},{or}} \right. \\ \; & \; & \; & {\left. {psoriasis} \right) = {yes}} \\ \; & + & 1.0104 & {{{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{cancer}},{{haematological} = {yes}}} \\ \; & + & 0.2436 & {{{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{cancer}},{{{non}\text{-}{haematological}} = {yes}}} \\ \; & + & 0.3863 & {{{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{diabetes}} = {yes}} \\ \; & + & 0.3064 & {{{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{hypertension}} = {yes}} \\ \; & + & 1.2642 & {{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{repiratory}\mspace{14mu}{disease}\mspace{14mu}\left( {{other}\mspace{14mu}{than}} \right.} \\ \; & \; & \; & {\left. {asthma} \right) = {yes}} \end{matrix}$

In an embodiment of the above formula, the starting ln(risk) (model intercept) is −0.5284 to 1.5509, or −0.16 to −0.36.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 18 to 29 is −1.5 to −1, or −1.4 to −1.2.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 30 to 39 is −1 to −0.7, or −0.9 to −0.8.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 40 to 49 is −0.6 to −0.2, or −0.45 to −0.35.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 60 to 69 is −0.4021263 to 0.2075385, or −0.19 to 0.09.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 70+ is 0.1504677 to 0.73339, or 0.34 to 0.54.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for males is −0.140599 to 0.3115929, or −0.3 to 0.19.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for non-Caucasians is −0.3029713 to 0.3837958, or −0.06 to 0.14.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for A blood type is −0.3018427 to 0.1791056, or −0.16 to 0.04.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for B blood type is −0.1817567 to 0.5895909, or 0.1 to 0.3.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for AB blood type is −1.172319 to 0.0641862, or −0.45 to −0.65.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, rheumatoid arthritis, lupus or psoriasis is −0.0309265 to 1.115784, or 0.44 to 0.64.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a haematological cancer is 0.1211918 to 1.899663, or 0.9 to 1.1.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a non-haematological cancer is −0.0625866 to 0.5498824, or 0.14 to 0.34.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, diabetes is 0.0624018 to 0.7101834, or 0.28 to 0.48.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, hypertension is 0.0504567 to 0.5623362, or 0.1 to 0.3.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a respiratory disease (excluding asthma) is 0.9775684 to 1.550944, or 1.16 to 1.36.

The present invention provides a method for assessing the risk of a human subject developing a severe response to a Coronavirus infection, the method comprising performing a clinical risk assessment of the human subject, wherein the clinical risk assessment involves determining at least the age and sex of the subject and producing a score. In an embodiment, the method further comprises comparing the score to a predetermined threshold, wherein if the score is at, or above, the threshold the subject is assessed at being at risk of developing a severe response to a Coronavirus infection.

In one embodiment, the subject is between 50 and 84 years of age and is asked their age and their sex.

In an embodiment, the method comprises determining the Log odds (LO). For example, the LO can be calculated using the formula:

LO=X+Σ Clinical β coefficients

In an embodiment, X is −2.25 to −1.25 or −2 or −1.5. In an embodiment, X is −1.749562.

In an embodiment, the relative risk is determined. In an embodiment, the relative risk is determined using the formula:

relative risk=e ^(LO)

In an embodiment, the probability is determined. In an embodiment, the probability is determined using the formula:

probability=e ^(LO)/(1+e ^(LO))

“e” is the mathematical constant that is the base of the natural logarithm.

In an embodiment, the probability obtained by the above formula is multiplied by 100 to obtain a percent chance of a severe response to a Coronavirus infection such as hospitalisation being required.

In an embodiment, if the subject is between 50 and 64 years of age they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0.

In an embodiment, if the subject is between 65 and 69 years of age they are assigned a β coefficient of 0 to 1, or 0.25 to 0.75 or 0.4694892.

In an embodiment, if the subject is between 70 and 74 years of age they are assigned a β coefficient of 0.5 to 1.5, or 0.75 to 1.25 or 1.006561.

In an embodiment, if the subject is between 75 and 79 years of age they are assigned a β coefficient of 0.9 to 1.9, or 1.15 to 1.65 or 1.435318.

In an embodiment, if the subject is between 80 and 84 years of age they are assigned a β coefficient of 1.1 to 2.1, or 1.35 to 1.85 or 1.599188.

In an embodiment, if the subject is female they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0.

In an embodiment, if the subject is male they are assigned a β coefficient of −0.1 to 0.9, or 0.15 to 0.65 or 0.3911169.

In an embodiment, the last value provided above in each criteria is used.

In an embodiment, the clinical risk assessment includes obtaining information from the subject on one or more or all of age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).

In an embodiment, each of the above factors are assessed and

-   -   LO=X+Σ Clinical β coefficients, where X is −1.8 to −0.8 or −1.6         or −1.15 or −1.36523;     -   if the subject is between 50 and 69 years of age they are         assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject is between 70 and 74 years of age they are         assigned a β coefficient of 0.1 to 1.1, or 0.35 to 0.85 or         0.5747727;     -   if the subject is between 75 and 79 years of age they are         assigned a β coefficient of 0.3 to 1.3, or −0.55 to 1.05 or         0.8243711;     -   if the subject is between 18 and 29 years of age they are         assigned a β coefficient of −2.3 to −0.3, or −0.18 to −0.8 or         −1.3111;     -   if the subject is between 30 and 39 years of age they are         assigned a β coefficient of −1.8 to 0.2, or −1.23 to −0.3 or         −0.8348;     -   if the subject is between 40 and 49 years of age they are         assigned a β coefficient of −1.4 to 0.6, or −0.9 to −0.1 or         −0.4038;     -   if the subject is between 80 and 84 years of age they are         assigned a β coefficient of 0.5 to 1.5, or 0.25 to 1.25 or         1.013973;     -   if the subject is female they are assigned a β coefficient of         −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject is male they are assigned a β coefficient of         −0.25 to 0.75, or 0 to 0.5 or 0.2444891;     -   if the subject is Caucasian they are assigned a β coefficient of         −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject is an ethnicity other than Caucasian they are         assigned a β coefficient of −0.2 to 0.8, or 0.05 to 1.55 or         0.29311;     -   the subjects height (in metres (m)) and weight (in kilograms         (kg)) is applied to the formula: (10 times m²) divided by kg,         which is multiplied by −1.1 to 2.1, or −1.35 to −1.85, or         −1.602056 to provide the β coefficient to be assigned;     -   if the subject has ever been diagnosed as having a         cerebrovascular disease they are assigned a β coefficient of         −0.1 to 0.9, or 0.15 to 0.65 or 0.4041337;     -   if the subject has not ever been diagnosed as having a         cerebrovascular disease they are assigned a β coefficient of         −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having a chronic         kidney disease they are assigned a β coefficient of 0.2 to 1.2,         or 0.55 to 0.95 or 0.6938494;     -   if the subject has not ever been diagnosed as having a chronic         kidney disease they are assigned a β coefficient of −0.5 to 0.5,         or −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having diabetes they         are assigned a β coefficient of −0.1 to 0.9, or 0.15 to 0.65 or         0.4297612;     -   if the subject has not ever been diagnosed as having diabetes         they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to         0.25 or 0;     -   if the subject has ever been diagnosed as having haematological         cancer they are assigned a β coefficient of 0.5 to 1.5, or 0.75         to 1.25 or 1.003877;     -   if the subject has not ever been diagnosed as having         haematological cancer they are assigned a β coefficient of −0.5         to 0.5, or −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having hypertension         they are assigned a β coefficient of −0.2 to 0.8, or 0.05 to         1.55 or 0.2922307;     -   if the subject has not ever been diagnosed as having         hypertension they are assigned a β coefficient of −0.5 to 0.5,         or −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having a         non-haematological cancer they are assigned a β coefficient of         −0.25 to 1, or 0 to 0.5 or 0.2558464;     -   if the subject has not ever been diagnosed as having a         non-haematological cancer they are assigned a β coefficient of         −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having a respiratory         disease (other than asthma) they are assigned a β coefficient of         0.7 to 1.7, or 0.95 to 1.45 or 1.173753; and     -   if the subject has ever been diagnosed as having a respiratory         disease (other than asthma) they are assigned a β coefficient of         −0.5 to 0.5, or −0.25 to 0.25 or 0.

In an embodiment, the last value provided above in each criteria is used.

In an embodiment, the clinical risk assessment includes obtaining information from the subject on one or more or all of age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).

In an embodiment, each of the above factors are assessed and

-   -   LO=X+Σ Clinical β coefficients, where X is −2 to −1.5 or −1.75         or −1.25 or −1.469939;     -   if the subject is between 50 and 64 years of age they are         assigned a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject is between 65 and 69 years of age they are         assigned a β coefficient of −0.3 to 0.7, or −0.05 to 0.45 or         0.1677566;     -   if the subject is between 70 and 74 years of age they are         assigned a β coefficient of 0.1 to 1.1, or 0.35 to 1.85 or         0.6352682;     -   if the subject is between 75 and 79 years of age they are         assigned a β coefficient of 0.4 to 1.4, or 0.65 to 1.15 or         0.8940548;     -   if the subject is between 80 and 84 years of age they are         assigned a β coefficient of 0.5 to 1.5, or 0.25 to 1.25 or         1.082477;     -   if the subject is female they are assigned a β coefficient of         −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject is male they are assigned a β coefficient of         −0.25 to 0.75, or 0 to 0.5 or 0.2418454;     -   if the subject is Caucasian they are assigned a β coefficient of         −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject is an ethnicity other than Caucasian they are         assigned a β coefficient of −0.2 to 0.8, or 0.05 to 1.55 or         0.2967777;     -   if the subject has a blood type other than ABO they are assigned         a β coefficient of −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject has an ABO blood type they are assigned a β         coefficient of −0.25 to 0.75, or 0 to 0.5 or −0.229737;     -   the subjects height (in metres (m)) and weight (in kilograms         (kg)) is applied to the formula: (10 times m²) divided by kg,         which is multiplied by −1.1 to 2.1, or −1.35 to −1.85, or         −1.560943 to provide the β coefficient to be assigned;     -   if the subject has ever been diagnosed as having a         cerebrovascular disease they are assigned a β coefficient of         −0.1 to 0.9, or 0.15 to 0.65 or 0.3950113;     -   if the subject has not ever been diagnosed as having a         cerebrovascular disease they are assigned a β coefficient of         −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having a chronic         kidney disease they are assigned a β coefficient of 0.2 to 1.2,         or 0.55 to 0.95 or 0.6650257;     -   if the subject has not ever been diagnosed as having a chronic         kidney disease they are assigned a β coefficient of −0.5 to 0.5,         or −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having diabetes they         are assigned a β coefficient of −0.1 to 0.9, or 0.15 to 0.65 or         0.4126633;     -   if the subject has not ever been diagnosed as having diabetes         they are assigned a β coefficient of −0.5 to 0.5, or −0.25 to         0.25 or 0;     -   if the subject has ever been diagnosed as having haematological         cancer they are assigned a β coefficient of 0.5 to 1.5, or 0.75         to 1.25 or 1.001079;     -   if the subject has not ever been diagnosed as having         haematological cancer they are assigned a β coefficient of −0.5         to 0.5, or −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having hypertension         they are assigned a β coefficient of −0.2 to 0.8, or 0.05 to         1.55 or 0.2640989;     -   if the subject has not ever been diagnosed as having         hypertension they are assigned a β coefficient of −0.5 to 0.5,         or −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having an         immunocompromised disease they are assigned a β coefficient of         0.1 to 1.1, or 0.35 to 0.85 or 0.6033541;     -   if the subject has not ever been diagnosed as having an         immunocompromised disease they are assigned a β coefficient of         −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having liver disease         they are assigned a β coefficient of −0.2 to 0.8, or 0.05 to         1.55 or 0.2301902;     -   if the subject has not ever been diagnosed as having liver         disease they are assigned a β coefficient of −0.5 to 0.5, or         −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having a         non-haematological cancer they are assigned a β coefficient of         −0.25 to 1, or 0 to 0.5 or 0.2381579;     -   if the subject has not ever been diagnosed as having a         non-haematological cancer they are assigned a β coefficient of         −0.5 to 0.5, or −0.25 to 0.25 or 0;     -   if the subject has ever been diagnosed as having a respiratory         disease (other than asthma) they are assigned a β coefficient of         0.7 to 1.7, or 0.95 to 1.45 or 1.148496; and     -   if the subject has ever been diagnosed as having a respiratory         disease (other than asthma) they are assigned a β coefficient of         −0.5 to 0.5, or −0.25 to 0.25 or 0.

In an embodiment, the last value provided above in each criteria is used.

In an embodiment, the subject's body mass index is determined using their height and weight.

In an embodiment, if any of the clinical factors are unknown, or the subject is unwilling to supply the relevant details, that factor(s) is assigned a β coefficient of 0.

In an embodiment, one or more or all of the clinical factors are self-assessed (self-reported). In an embodiment, the race/ethnicity is self-assessed (self-reported). In an embodiment, one or more or all of current or previous disease status, such as an autoimmune disease, an haematological cancer, an non-haematological cancer, diabetes, hypertension or a respiratory disease, is self-assessed (self-reported).

In an embodiment, the clinical assessment comprises determining the blood type of the subject. This will typically comprise obtaining a sample comprising blood from the subject. The detection method used can any be any suitable method known in the art. In embodiment, a genetic test as described in the Examples is used, preferably concurrently with a genetic analysis for assessing the risk of a human subject developing a severe response to a coronavirus infection.

For instance, ABO blood type can be imputed using three SNPs, namely rs505922, rs8176719 and rs8176746) in the ABO gene on chromosome 9q34.2. An rs8176719 deletion (or for those with no result for rs8176719, a T allele at rs505922) indicates haplotype O. At rs8176746, haplotype A is indicated by the presence of the G allele and haplotype B is indicated by the presence of the T allele (see Table 7).

TABLE 7 SNPS and ABO Imputation. rs8176719 rs505922 rs8176746 Genotype Phenotype T/T OO O TC/T C/C (G/G) AO A TC/T C/A (G/T) BO B TC/T A/A (T/T) BO B TC/TC C/C (G/G) AA A TC/TC A/A (T/T) BB B TC/TC C/A (G/T) AB AB missing T/T OO O missing C/T C/C (G/G) AO A missing C/T C/A (G/T) BO B missing C/T A/A (T/T BO B missing C/C C/C (G/G) AA A missing C/C A/A (T/T) BB B missing C/C C/A (G/T) AB AB

In an embodiment, whether a subject has or has had (also referred to herein as “has ever been diagnosed”) with a particular disease state, the disease is classified using the international Classification of Disease (ICD) system. Thus,

-   -   asthma is as per ICD9 (493*) and ICD10 (J45* and J46),     -   an autoimmune (rheumatoid/lupus/psoriasis) is as per ICD9 (954,         696*, 7100, 714, 7140* and 7142* and ICD10 (J990, L40*, L41*,         M05*-M07* and M32*),     -   a haematological cancer is as per ICD9 (200*-208*) and ICD10         (C81*-C86*, C88* and C90*-C96*),     -   a non-haematological cancer is as per ICD9 (140*-165*,         169*-175*, 179*-195* and 196*-199*) and ICD10 (C00*-C26*,         C30*-C34*, C37*-058*, C60*-C80*, C97*),     -   a cerebrovascular disease is as per ICD9 (430*-438*) and ICD10         (G46* and I60*-I69*),     -   diabetes is as per ICD9 (250*) and ICD10 (E10*-E14*),     -   heart disease is as per ICD9 (413*-416*, V422, V432-V434) and         ICD10 (I20*-I25*, I48*, Z95*),     -   hypertension is as per ICD9 (401*, 405*, 6420-6422) and ICD10         (I10*, I15*, O10*),     -   an immunocompromised disease is as per ICD9 (V420, V421, V426,         V427, V429, 042, 043, 044, 279, 2790*) and ICD10 (B20*-B24,         D80*-D84*, Z940-Z944, Z949),     -   a kidney disease is as per ICD9 (585*) and ICD10 (N18*),     -   liver disease is as per ICD9 (571*) and ICD10 (K70*-K77*), and     -   a respiratory disease (excluding asthma) is as per ICD9         (494*-496*, 500*, 501*-508*, 491*, 492*, 496*) and ICD10         (J60*-J70*, J80*-J82, J84*-J86*, J90-J96*, J98*, J41*-J44*).

Combined Clinical Assessment and Genetic Assessment

In an embodiment, to obtain the “risk” of a human subject developing a severe response to a Coronavirus infection, the following formula can be used:

$\begin{matrix} {\ln\left( {risk} \right)} & {= {{Model}\mspace{14mu}{Intercept}}} \\  + & {{OR}\mspace{14mu} x\mspace{14mu}{percentage}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{risk}\mspace{14mu}{alleles}} \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{clinical}\mspace{14mu}{factor}\mspace{14mu}{one}\mspace{14mu}{applies}} \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{clinical}\mspace{14mu}{factor}\mspace{14mu}{two}\mspace{14mu}{applies}} \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{clinical}\mspace{14mu}{factor}\mspace{14mu}{three}\mspace{14mu}{applies}} \\ \ldots & \; \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{clinical}\mspace{14mu}{factor}\mspace{14mu} n\mspace{14mu}{applies}} \end{matrix}$

Where OR=Odds Ratio.

In an embodiment, to obtain the “risk” of a human subject developing a severe response to a Coronavirus infection, the following formula can be used:

$\begin{matrix} {\ln\left( {risk} \right)} & {= {{Model}\mspace{14mu}{Intercept}}} \\  + & {{OR}\mspace{14mu} x\mspace{14mu}{percentage}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{risk}\mspace{14mu}{alleles}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {18\text{-}29\mspace{14mu}{years}\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {30\text{-}39\mspace{14mu}{years}\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {40\text{-}49\mspace{14mu}{years}\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {60\text{-}69\mspace{14mu}{years}\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {70\text{+}\mspace{14mu}{year}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{gender}} = {male}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{ethnicity}} = {{non}\text{-Caucasian}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = {A\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = {B\mspace{14mu}{or}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = {A\; B}} \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{autoimmune}\mspace{14mu}{disease}\mspace{20mu}\left( {{namely},} \right.} \\ \; & {\left. {{{rheumatoid}\mspace{14mu}{arthritis}},{lupus},{{or}\mspace{14mu}{psoriasis}}} \right) = {yes}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{cancer}},{{haematological} = {yes}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{cancer}},{{{non}\text{-}{haematological}} = {yes}}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{diabetes}} = {yes}} \\  + & {{{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{hypertension}} = {yes}} \\  + & {{OR}\mspace{14mu}{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{repiratory}\mspace{14mu}{disease}\mspace{14mu}\left( {{other}\mspace{14mu}{than}} \right.} \\ \; & {\left. {asthma} \right) = {yes}} \end{matrix}$

Where OR=Odds Ratio

Using the above formulae the relative risk of a human subject developing a severe response to a Coronavirus infection is: risk=

.

In one example, to obtain the “risk” of a human subject developing a severe response to a Coronavirus infection, the following formula can be used:

$\begin{matrix} {\ln\left( {risk} \right)} & = & {- 10.7657} & \; \\ \; & + & 0.1717 & {x\mspace{14mu}{percentage}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{risk}\mspace{14mu}{alleles}} \\ \; & + & {- 1.3111} & {{{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {18\text{-}29\mspace{14mu}{years}}} \\ \; & + & {- 0.8348} & {{{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {30\text{-}39\mspace{14mu}{years}}} \\ \; & + & {- 0.4038} & {{{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {40\text{-}49\mspace{14mu}{years}}} \\ \; & + & {- 0.0600} & {{{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {60\text{-}69\mspace{14mu}{years}}} \\ \; & + & 0.5325 & {{{if}\mspace{14mu}{age}\mspace{14mu}{group}} = {70\text{+}\mspace{14mu}{year}}} \\ \; & + & 0.1387 & {{{if}\mspace{14mu}{gender}} = {male}} \\ \; & + & 0.3542 & {{{if}\mspace{14mu}{ethnicity}} = {{non}\text{-Caucasian}}} \\ \; & + & {- 0.2164} & {{{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = A} \\ \; & + & {- 0.1712} & {{{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = B} \\ \; & + & {- 0.8746} & {{{if}\mspace{14mu}{ABO}\mspace{14mu}{blood}\mspace{14mu}{type}} = {A\; B}} \\ \; & + & 0.7876 & {{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{autoimmune}\mspace{14mu}{disease}} \\ \; & \; & \; & \left( {{namely},{{rheumatoid}\mspace{14mu}{arthritis}},{lupus},{or}} \right. \\ \; & \; & \; & {\left. {psoriasis} \right) = {yes}} \\ \; & + & 1.0375 & {{{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{cancer}},{{haematological} = {yes}}} \\ \; & + & 0.3667 & {{{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{cancer}},{{{non}\text{-}{haematological}} =}} \\ \; & \; & \; & {yes} \\ \; & + & 0.4890 & {{{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{diabetes}} = {yes}} \\ \; & + & 0.3034 & {{{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{hypertension}} = {yes}} \\ \; & + & 1.2331 & {{if}\mspace{14mu}{has}\text{/}{had}\mspace{14mu}{repiratory}\mspace{14mu}{disease}\mspace{14mu}\left( {{other}\mspace{14mu}{than}} \right.} \\ \; & \; & \; & {\left. {asthma} \right) = {yes}} \end{matrix}$

Using this formula the relative risk of a human subject developing a severe response to a Coronavirus infection is: risk=

.

In an embodiment of the above formula, the starting ln(risk) (model intercept) is −12.5559 to −8.9755, or −12 to −8, or −11 to −10.5.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for the percentage of risk alleles is 0.142 to 0.2006, or 0.16 to 0.18.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 18 to 29 is −1.5 to −1, or −1.4 to −1.2.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 30 to 39 is −1 to −0.7, or −0.9 to −0.8.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 40 to 49 is −0.6 to −0.2, or −0.45 to −0.35.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 60 to 69 is −0.3819 to 0.2619, or −0.1 to 0.1.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for ages 70+ is 0.2213 to 0.8438, or 0.43 to 0.63.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for males is −0.1005 to 0.3779, or 0.03 to 0.23.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for non-Caucasians is −0.0084 to 0.7167, or 0.25 to 0.45.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for A blood type is −0.4726 to 0.0397, or −0.11 to −0.31.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for B blood type is −0.2348 to 0.5773, or 0.07 to 0.27.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for AB blood type is −1.5087 to −0.2404, or −0.77 to −0.97.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, rheumatoid arthritis, lupus or psoriasis is 0.1832 to 1.3920, or 0.68 to 0.88.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a haematological cancer is 0.0994 to 1.9756, or 0.93 to 1.13.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a non-haematological cancer is 0.0401 to 0.6933, or 0.26 to 0.46.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, diabetes is 0.1450 to 0.8330, or 0.39 to 0.59.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, hypertension is 0.0313 to 0.5756, or 0.2 to 0.4.

In an embodiment of the above formula, the adjustment of the starting ln(risk) for a human who has, or has had, a respiratory disease (excluding asthma) is 0.9317 to 0.1535, or 1.13 to 1.33.

In an alternate embodiment, and as outlined above, the method comprises determining the Log odds (LO). For example, the LO can be calculated using the formula:

LO=X+SRF+Σ Clinical β coefficients

In an embodiment, the SRF is the SNP Risk Factor which is: (No of Risk Alleles×SNP β coefficient).

In an embodiment, the relative risk is determined. In an embodiment, the relative risk is determined using the formula:

relative risk=e^(LO)

In an embodiment, the probability is determined. In an embodiment, the probability is determined using the formula:

probability=e ^(LO)/(1+e ^(LO))

“e” is the mathematical constant that is the base of the natural logarithm.

In an embodiment, the probability obtained by the above formula is multiplied by 100 to obtain a percent chance of a severe response to a Coronavirus infection such as hospitalisation being required.

In an embodiment, the genetic risk assessment involves the analysis of rs10755709, rs112317747, rs112641600, rs118072448, rs2034831, rs7027911 and rs71481792. In an embodiment, X is −1.8 to −0.8 or −1.6 or −1.15. In an embodiment, X is −1.36523. In an embodiment, the subject is assigned a β coefficient of −0.08 to 0.32, or 0.02 to 0.22 or 0.124239 for each G (risk) allele present at rs10755709. Thus, for example, if the subject is homozygous for the risk allele they can be assigned a β coefficient of 0.248478, if they are heterozygous can be assigned a β coefficient of 0.124239, and if they is homozygous for the non-risk allele (C at rs10755709) they can be assigned a β coefficient of 0.248478. In an embodiment, the subject is assigned a β coefficient of 0.07 to 0.47, or 0.17 to 0.37 or 0.2737487 for each C (risk) allele present at rs112317747. In an embodiment, the subject is assigned a β coefficient of −0.43 to −0.03, or −0.33 to −0.13 or −0.2362513 for each T (risk) allele present at rs112641600. In an embodiment, the subject is assigned a β coefficient of −0.4 to 0, or −0.3 to −0.1 or −0.1995879 for each C (risk) allele present at rs118072448. In an embodiment, the subject is assigned a β coefficient of 0.04 to 0.44, or 0.14 to 0.34 or 0.2371955 for each C (risk) allele present at rs2034831. In an embodiment, the subject is assigned a β coefficient of −0.1 to 0.3, or 0 to 0.2 or 0.1019074 for each A (risk) allele present at rs7027911. In an embodiment, the subject is assigned a β coefficient of −0.3 to 0.1, or −0.2 to 0 or −0.1058025 for each T (risk) allele present at rs71481792. In an embodiment, the Clinical β coefficients is determined as above such as factoring in β coefficients for each of age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).

In an embodiment, the genetic risk assessment involves the analysis of rs10755709, rs112317747, rs112641600, rs118072448, rs2034831, rs7027911, rs71481792, rs115492982 and rs1984162. In an embodiment, X is −2 to −1.5 or −1.75 or −1.25. In an embodiment, X is −1.469939. In an embodiment, the subject is assigned a β coefficient of −0.08 to 0.32, or 0.02 to 0.22 or 0.1231766 for each G (risk) allele present at rs10755709. Thus, for example, if the subject is homozygous for the risk allele they can be assigned a β coefficient of 0.2463532, if they are heterozygous can be assigned a β coefficient of 0.1231766, and if they is homozygous for the non-risk allele (C at rs10755709) they can be assigned a β coefficient of 0.248478. In an embodiment, the subject is assigned a β coefficient of 0.06 to 0.46, or 0.16 to 0.36 or 0.2576692 for each C (risk) allele present at rs112317747. In an embodiment, the subject is assigned a β coefficient of −0.43 to −0.03, or −0.33 to −0.13 or −0.2384001 for each T (risk) allele present at rs112641600. In an embodiment, the subject is assigned a β coefficient of −0.4 to 0, or −0.3 to −0.1 or −0.1965609 for each C (risk) allele present at rs118072448. In an embodiment, the subject is assigned a β coefficient of 0.04 to 0.44, or 0.14 to 0.34 or 0.2414792 for each C (risk) allele present at rs2034831. In an embodiment, the subject is assigned a β coefficient of −0.1 to 0.3, or 0 to 0.2 or 0.0998459 for each A (risk) allele present at rs7027911. In an embodiment, the subject is assigned a β coefficient of −0.3 to 0.1, or −0.2 to 0 or −0.1032044 for each T (risk) allele present at rs71481792. In an embodiment the subject is assigned a β coefficient of 0.21 to 0.61, or 0.31 to 0.51 or 0.4163575 for each A (risk) allele present at rs115492982. In an embodiment the subject is assigned a β coefficient of −0.1 to 0.3, or 0 to 0.2 or 0.1034362 for each A (risk) allele present at rs1984162. In an embodiment, the Clinical β coefficients is determined as above such as factoring in β coefficients for each of age, gender, race/ethnicity, blood type, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an haematological cancer, does the human have or has had an immunocompromised disease, does the human have or has had an haematological cancer, does the human have or has had liver disease, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma).

Any of the above calculations can be performed for non-SNP polymorphisms or a combination thereof.

In another embodiment, when combining the clinical risk assessment with the genetic risk assessment to obtain the “risk” of a human subject developing a severe response to a Coronavirus infection, the following formula can be used:

[Risk (i.e. Clinical Evaluation×SNP risk)]=[Clinical Evaluation risk]×SNP₁×SNP₂×SNP₃×SNP₄×SNP₅×SNP₆×SNP₇,×SNP₈,×SNP_(N) etc.

Where Clinical Evaluation is the risk provided by the clinical evaluation, and SNP₁ to SNP_(N) are the relative risk for the individual SNPs, each scaled to have a population average of 1 as outlined above. Because the SNP risk values have been “centred” to have a population average risk of 1, if one assumes independence among the SNPs, then the population average risk across all genotypes for the combined value is consistent with the underlying Clinical Evaluation risk estimate.

In an embodiment, the genetic risk assessment is combined with the clinical risk assessment to obtain the “relative risk” of a human subject developing a severe response to a Coronavirus infection.

A threshold(s) can be set as described above when genetic risk is assessed alone. In one example, the threshold could be set to be at least 5, at least 6, at least 7, at least 8, at least 9 or at least 10, when using the embodiment of the test described in Example 5. If set at 5 in this example, about 10% of the UK biobank population have a risk score over 5.0 resulting in the following performance characteristics for the test:

Sensitivity 38.41% Specificity 93.79%

Positive predictive value 91.78% Negative predictive value 45.76%

As the skilled person would understand, various different thresholds could be set altering performance depending on the level of risk the entity conducting the test is willing accept.

Depending upon the end-usage of the test, a threshold may be altered to the most appropriate values.

Marker Detection Strategies

Amplification primers for amplifying markers (e.g., marker loci) and suitable probes to detect such markers or to genotype a sample with respect to multiple marker alleles, can be used in the disclosure. For example, primer selection for long-range PCR is described in U.S. Ser. No. 10/042,406 and U.S. Ser. No. 10/236,480; for short-range PCR, U.S. Ser. No. 10/341,832 provides guidance with respect to primer selection. Also, there are publicly available programs such as “Oligo” available for primer design. With such available primer selection and design software, the publicly available human genome sequence and the polymorphism locations, one of skill can construct primers to amplify the polymorphisms to practice the disclosure. Further, it will be appreciated that the precise probe to be used for detection of a nucleic acid comprising a polymorphism (e.g., an amplicon comprising the polymorphism) can vary, e.g., any probe that can identify the region of a marker amplicon to be detected can be used in conjunction with the present disclosure. Further, the configuration of the detection probes can, of course, vary. Thus, the disclosure is not limited to the sequences recited herein.

Examples of primer pairs for detecting some of the SNP's disclosed herein include: rs11549298 (ACCTGGTATCAGTGAAGAGGATCAG (SEQ ID NO:1) and TCTTGATACAACTGTAAGAAGTGGT (SEQ ID NO:2)), rs112317747 (TATTTCTTTGTTGCCCTCTATCTCT (SEQ ID NO:3) and GAAAGAGATGGGTTGGCATTATTAT (SEQ ID NO:4)), rs2034831 (TAAAATTAGAACTGGAGGGCTGGGT (SEQ ID NO:5) and TGGCATTATAAACACTCACTGAAGT (SEQ ID NO: 6)), rs112641600 (AATGCCATCTGATGAGAGAAGTTTT (SEQ ID NO:7) and TACAGTTTTAAAAATGGGCGTTTCT (SEQ ID NO:8)), rs10755709 (TATAATAACACGTGGAAGTGAAAAT (SEQ ID NO:9) and TTGTTTGTATGTGTGAAATGATTCT (SEQ ID NO:10)), rs118072448 (AAGCAAACTATTCTTCAGGAATCCA (SEQ ID NO:11) and ATTTCTGCATTTCACTTTGTGTGGT (SEQ ID NO:12)), rs7027911 (GTAAATGCTGCTAACAGAGCTCTTT (SEQ ID NO:13) and GAAGAGAGTTTATTAGCAAGGCCTC (SEQ ID NO:14)), rs71481792 (CATTTGGGAAAAGCCACTGAATGGA (SEQ ID NO:15) and AGATTGACTAGCCGTTGAGAGTAGA (SEQ ID NO:16)), and rs1984162 (ACTGACTCCTGACACTCTTGAAGCG (SEQ ID NO:17) and GACTCTTCTCTGGCATCTTCTCATG (SEQ ID NO:18)).

Indeed, it will be appreciated that amplification is not a requirement for marker detection, for example one can directly detect unamplified genomic DNA simply by performing a Southern blot on a sample of genomic DNA.

Typically, molecular markers are detected by any established method available in the art, including, without limitation, allele specific hybridization (ASH), detection of extension, array hybridization (optionally including ASH), or other methods for detecting polymorphisms, amplified fragment length polymorphism (AFLP) detection, amplified variable sequence detection, randomly amplified polymorphic DNA (RAPD) detection, restriction fragment length polymorphism (RFLP) detection, self-sustained sequence replication detection, simple sequence repeat (SSR) detection, and single-strand conformation polymorphisms (SSCP) detection.

Some techniques for detecting genetic markers utilize hybridization of a probe nucleic acid to nucleic acids corresponding to the genetic marker (e.g., amplified nucleic acids produced using genomic DNA as a template). Hybridization formats, including, but not limited to: solution phase, solid phase, mixed phase, or in situ hybridization assays are useful for allele detection. An extensive guide to the hybridization of nucleic acids is found in Tijssen (1993) Laboratory Techniques in Biochemistry and Molecular Biology—Hybridization with Nucleic Acid Probes Elsevier, New York, as well as in Sambrook et al. (supra).

PCR detection using dual-labelled fluorogenic oligonucleotide probes, commonly referred to as “TaqMan™” probes, can also be performed according to the present disclosure. These probes are composed of short (e.g., 20-25 base) oligodeoxynucleotides that are labelled with two different fluorescent dyes. On the 5′ terminus of each probe is a reporter dye, and on the 3′ terminus of each probe a quenching dye is found. The oligonucleotide probe sequence is complementary to an internal target sequence present in a PCR amplicon. When the probe is intact, energy transfer occurs between the two fluorophores and emission from the reporter is quenched by the quencher by FRET. During the extension phase of PCR, the probe is cleaved by 5′ nuclease activity of the polymerase used in the reaction, thereby releasing the reporter from the oligonucleotide-quencher and producing an increase in reporter emission intensity. Accordingly, TaqMan™ probes are oligonucleotides that have a label and a quencher, where the label is released during amplification by the exonuclease action of the polymerase used in amplification. This provides a real time measure of amplification during synthesis. A variety of TaqMan™ reagents are commercially available, e.g., from Applied Biosystems (Division Headquarters in Foster City, Calif.) as well as from a variety of specialty vendors such as Biosearch Technologies (e.g., black hole quencher probes). Further details regarding dual-label probe strategies can be found, e.g., in WO 92/02638.

Other similar methods include e.g. fluorescence resonance energy transfer between two adjacently hybridized probes, e.g., using the “LightCycler®” format described in U.S. Pat. No. 6,174,670.

Array-based detection can be performed using commercially available arrays, e.g., from Affymetrix (Santa Clara, Calif.) or other manufacturers. Reviews regarding the operation of nucleic acid arrays include Sapolsky et al. (1999); Lockhart (1998); Fodor (1997a); Fodor (1997b) and Chee et al. (1996). Array based detection is one preferred method for identification markers of the disclosure in samples, due to the inherently high-throughput nature of array based detection.

The nucleic acid sample to be analysed is isolated, amplified and, typically, labelled with biotin and/or a fluorescent reporter group. The labelled nucleic acid sample is then incubated with the array using a fluidics station and hybridization oven. The array can be washed and or stained or counter-stained, as appropriate to the detection method. After hybridization, washing and staining, the array is inserted into a scanner, where patterns of hybridization are detected. The hybridization data are collected as light emitted from the fluorescent reporter groups already incorporated into the labelled nucleic acid, which is now bound to the probe array. Probes that most clearly match the labelled nucleic acid produce stronger signals than those that have mismatches. Since the sequence and position of each probe on the array are known, by complementarity, the identity of the nucleic acid sample applied to the probe array can be identified.

Markers and polymorphisms can also be detected using DNA sequencing. DNA sequencing methods are well known in the art and can be found for example in Ausubel et al, eds., Short Protocols in Molecular Biology, 3rd ed., Wiley, (1995) and Sambrook et al, Molecular Cloning, 2nd ed., Chap. 13, Cold Spring Harbor Laboratory Press, (1989). Sequencing can be carried out by any suitable method, for example, dideoxy sequencing, chemical sequencing, or variations thereof.

Suitable sequencing methods also include Second Generation, Third Generation, or Fourth Generation sequencing technologies, all referred to herein as “next generation sequencing”, including, but not limited to, pyrosequencing, sequencing-by-ligation, single molecule sequencing, sequence-by-synthesis (SBS), massive parallel clonal, massive parallel single molecule SBS, massive parallel single molecule real-time, massive parallel single molecule real-time nanopore technology, etc. A review of some such technologies can be found in (Morozova and Marra, 2008), herein incorporated by reference. Accordingly, in some embodiments, performing a genetic risk assessment as described herein involves detecting the at least two polymorphisms by DNA sequencing. In an embodiment, the at least two polymorphisms are detected by next generation sequencing.

Next generation sequencing (NGS) methods share the common feature of massively parallel, high-throughput strategies, with the goal of lower costs in comparison to older sequencing methods (see, Voelkerding et al., 2009; MacLean et al., 2009).

A number of such DNA sequencing techniques are known in the art, including fluorescence-based sequencing methodologies. In some embodiments, automated sequencing techniques are used. In some embodiments, parallel sequencing of partitioned amplicons is used (WO2006084132). In some embodiments, DNA sequencing is achieved by parallel oligonucleotide extension (See, e.g., U.S. Pat. Nos. 5,750,341 and 6,306,597). Additional examples of sequencing techniques include the Church polony technology (Mitra et al., 2003; Shendure et al., 2005; U.S. Pat. Nos. 6,432,36; 6,485,944; 6,511,803), the 454 picotiter pyrosequencing technology (Margulies et al., 2005; US 20050130173), the Solexa single base addition technology (Bennett et al., 2005; U.S. Pat. Nos. 6,787,308; 6,833,246), the Lynx massively parallel signature sequencing technology (Brenner et al., 2000; U.S. Pat. Nos. 5,695,934; 5,714,330), and the Adessi PCR colony technology (Adessi et al., 2000).

Correlating Markers to Phenotypes

These correlations can be performed by any method that can identify a relationship between an allele and a phenotype, or a combination of alleles and a combination of phenotypes. For example, alleles defined herein can be correlated with a severe response to Coronavirus infection phenotypes. The methods can involve referencing a look up table that comprises correlations between alleles of the polymorphism and the phenotype. The table can include data for multiple allele-phenotype relationships and can take account of additive or other higher order effects of multiple allele-phenotype relationships, e.g., through the use of statistical tools such as principle component analysis, heuristic algorithms, etc.

Correlation of a marker to a phenotype optionally includes performing one or more statistical tests for correlation. Many statistical tests are known, and most are computer-implemented for ease of analysis. A variety of statistical methods of determining associations/correlations between phenotypic traits and biological markers are known and can be applied to the present disclosure (Hartl et al., 1981). A variety of appropriate statistical models are described in Lynch and Walsh (1998). These models can, for example, provide for correlations between genotypic and phenotypic values, characterize the influence of a locus on a phenotype, sort out the relationship between environment and genotype, determine dominance or penetrance of genes, determine maternal and other epigenetic effects, determine principle components in an analysis (via principle component analysis, or “PCA”), and the like. The references cited in these texts provides considerable further detail on statistical models for correlating markers and phenotype.

In addition to standard statistical methods for determining correlation, other methods that determine correlations by pattern recognition and training, such as the use of genetic algorithms, can be used to determine correlations between markers and phenotypes. This is particularly useful when identifying higher order correlations between multiple alleles and multiple phenotypes. To illustrate, neural network approaches can be coupled to genetic algorithm-type programming for heuristic development of a structure-function data space model that determines correlations between genetic information and phenotypic outcomes.

In any case, essentially any statistical test can be applied in a computer implemented model, by standard programming methods, or using any of a variety of “off the shelf” software packages that perform such statistical analyses, including, for example, those noted above and those that are commercially available, e.g., from Partek Incorporated (St. Peters, Mo.; www.partek.com), e.g., that provide software for pattern recognition (e.g., which provide Partek Pro 2000 Pattern Recognition Software).

Systems for performing the above correlations are also a feature of the disclosure. Typically, the system will include system instructions that correlate the presence or absence of an allele (whether detected directly or, e.g., through expression levels) with a predicted phenotype.

Optionally, the system instructions can also include software that accepts diagnostic information associated with any detected allele information, e.g., a diagnosis that a subject with the relevant allele has a particular phenotype. This software can be heuristic in nature, using such inputted associations to improve the accuracy of the look up tables and/or interpretation of the look up tables by the system. A variety of such approaches, including neural networks, Markov modelling, and other statistical analysis are described above.

Polymorphic Profiling

The disclosure provides methods of determining the polymorphic profile of an individual at the polymorphisms outlined in the present disclosure (e.g. Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22) or polymorphisms in linkage disequilibrium with one or more thereof.

The polymorphic profile constitutes the polymorphic forms occupying the various polymorphic sites in an individual. In a diploid genome, two polymorphic forms, the same or different from each other, usually occupy each polymorphic site. Thus, the polymorphic profile at sites X and Y can be represented in the form X (x1, x1), and Y (y1, y2), wherein x1, x1 represents two copies of allele x1 occupying site X and y1, y2 represent heterozygous alleles occupying site Y.

The polymorphic profile of an individual can be scored by comparison with the polymorphic forms associated with resistance or susceptibility to a severe response to a Coronavirus infection occurring at each site. The comparison can be performed on at least, e.g., 1, 2, 5, 10, 25, 50, or all of the polymorphic sites, and optionally, others in linkage disequilibrium with them. The polymorphic sites can be analysed in combination with other polymorphic sites.

Polymorphic profiling is useful, for example, in selecting agents to affect treatment or prophylaxis of a severe response to a Coronavirus infection in a given individual. Individuals having similar polymorphic profiles are likely to respond to agents in a similar way.

Polymorphic profiling is also useful for stratifying individuals in clinical trials of agents being tested for capacity to treat a severe response to a Coronavirus infection or related conditions. Such trials are performed on treated or control populations having similar or identical polymorphic profiles (see EP 99965095.5), for example, a polymorphic profile indicating an individual has an increased risk of developing a severe response to a Coronavirus infection. Use of genetically matched populations eliminates or reduces variation in treatment outcome due to genetic factors, leading to a more accurate assessment of the efficacy of a potential drug.

Polymorphic profiling is also useful for excluding individuals with no predisposition to a severe response to a Coronavirus infection from clinical trials. Including such individuals in the trial increases the size of the population needed to achieve a statistically significant result. Individuals with no predisposition to a severe response to a Coronavirus infection can be identified by determining the numbers of resistances and susceptibility alleles in a polymorphic profile as described above. For example, if a subject is genotyped at ten sites of the disclosure associated with a severe response to a Coronavirus infection, twenty alleles are determined in total. If over 50% and alternatively over 60% or 75% percent of these are resistance genes, the individual is unlikely to develop a severe response to a Coronavirus infection and can be excluded from the trial.

Computer Implemented Method

The methods of the present disclosure may be implemented by a system such as a computer implemented method. For example, the system may be a computer system comprising one or a plurality of processors which may operate together (referred to for convenience as “processor”) connected to a memory. The memory may be a non-transitory computer readable medium, such as a hard drive, a solid state disk or CD-ROM. Software, that is executable instructions or program code, such as program code grouped into code modules, may be stored on the memory, and may, when executed by the processor, cause the computer system to perform functions such as determining that a task is to be performed to assist a user to determine the risk of a human subject developing a severe response to a Coronavirus infection; receiving data indicating the clinical risk assessment and the genetic risk assessment of the human subject developing a severe response to a Coronavirus infection, wherein the genetic risk was derived by detecting at least two polymorphisms known to be associated with a severe response to a Coronavirus infection; processing the data to combine the clinical risk assessment and the genetic risk assessment to obtain the risk of a human subject developing a severe response to a Coronavirus infection; outputting the risk of a human subject developing a severe response to a Coronavirus infection.

For example, the memory may comprise program code which when executed by the processor causes the system to determine at least two polymorphisms known to be associated with a severe response to a Coronavirus infection; process the data to combine the clinical risk assessment and the genetic risk assessment to obtain the risk of a human subject developing a severe response to a Coronavirus infection; report the risk of a human subject developing a severe response to a Coronavirus infection.

In another embodiment, the system may be coupled to a user interface to enable the system to receive information from a user and/or to output or display information. For example, the user interface may comprise a graphical user interface, a voice user interface or a touchscreen.

In an embodiment, the program code may causes the system to determine the “Polymorphism risk”.

In an embodiment, the program code may causes the system to determine CombinedClinical Risk×Genetic Risk (for example Polymorphism risk).

In an embodiment, the system may be configured to communicate with at least one remote device or server across a communications network such as a wireless communications network. For example, the system may be configured to receive information from the device or server across the communications network and to transmit information to the same or a different device or server across the communications network. In other embodiments, the system may be isolated from direct user interaction.

In another embodiment, performing the methods of the present disclosure to assess the risk of a human subject developing a severe response to a Coronavirus infection, enables establishment of a diagnostic or prognostic rule based on the clinical risk assessment and the genetic risk assessment of the human subject developing a severe response to a Coronavirus infection. For example, the diagnostic or prognostic rule can be based on the Combined Clinical Risk×Genetic Risk score relative to a control, standard or threshold level of risk.

In another embodiment, the diagnostic or prognostic rule is based on the application of a statistical and machine learning algorithm. Such an algorithm uses relationships between a population of polymorphisms and disease status observed in training data (with known disease status) to infer relationships which are then used to determine the risk of a human subject developing a severe response to a Coronavirus infection in subjects with an unknown risk. An algorithm is employed which provides an risk of a human subject developing a severe response to a Coronavirus infection. The algorithm performs a multivariate or univariate analysis function.

Kits and Products

In an embodiment, the present disclosure provides a kit comprising at least two sets of primers for amplifying two or more nucleic acids, wherein the two or more nucleic acids comprise a polymorphism selected from any one of Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 sets of the primers for amplifying nucleic acids comprising a polymorphism selected from any one of Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 sets sets of the primers for amplifying nucleic acids comprising a polymorphism selected from Table 2 and Table 3, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, or at least 60 sets of the primers for amplifying nucleic acids comprising a polymorphism selected from Table 4 or Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, or at least 60 sets of the primers for amplifying nucleic acids comprising a polymorphism selected from Table 4, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50, sets of the primers for amplifying nucleic acids comprising a polymorphism selected from Table 3 or Table 6a, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50, sets of the primers for amplifying nucleic acids comprising a polymorphism selected from Table 3, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises sets of primers for amplifying nucleic acids comprising one or more or all of the polymorphisms provided in Table 19, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises sets of primers for amplifying nucleic acids comprising one or more or all of the polymorphisms provided in Table 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

As would be appreciated by those of skill in the art, once a polymorphism is identified, primers can be designed to amplify the polymorphism as a matter of routine. Various software programs are freely available that can suggest suitable primers for amplifying polymorphisms of interest.

Again, it would be known to those of skill in the art that PCR primers of a PCR primer pair can be designed to specifically amplify a region of interest from human DNA. Each PCR primer of a PCR primer pair can be placed adjacent to a particular single-base variation on opposing sites of the DNA sequence variation. Furthermore, PCR primers can be designed to avoid any known DNA sequence variation and repetitive DNA sequences in their PCR primer binding sites.

The kit may further comprise other reagents required to perform an amplification reaction such as a buffer, nucleotides and/or a polymerase, as well as reagents for extracting nucleic acids from a sample.

Array based detection is one preferred method for assessing the polymorphisms of the disclosure in samples, due to the inherently high-throughput nature of array based detection. A variety of probe arrays have been described in the literature and can be used in the context of the present disclosure for detection of polymorphisms that can be correlated to a severe response to a Coronavirus infection. For example, DNA probe array chips are used in one embodiment of the disclosure. The recognition of sample DNA by the set of DNA probes takes place through DNA hybridization. When a DNA sample hybridizes with an array of DNA probes, the sample binds to those probes that are complementary to the sample DNA sequence. By evaluating to which probes the sample DNA for an individual hybridizes more strongly, it is possible to determine whether a known sequence of nucleic acid is present or not in the sample, thereby determining whether a marker found in the nucleic acid is present.

Thus, in another embodiment, the present disclosure provides a genetic array comprising at least two sets of probes for hybridising to two or more nucleic acids, wherein the two or more nucleic acids comprise a polymorphism selected from any one of Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 sets of probes for hybridising a polymorphism selected from any one of Tables 1 to 3, 5a or 6, or Tables 1 to 6, 8, 19 or 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 250, at least 300 or at least 306 sets of probes for hybridising a polymorphism selected from Table 2 and Table 3, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, or at least 60 sets of probes for hybridising a polymorphism selected from Table 4 or Table 5, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40, at least 50, or at least 60 sets of probes for hybridising a polymorphism selected from Table 4, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50, sets of probes for hybridising a polymorphism selected from Table 3 or Table 6a, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 20, at least 30, at least 40 or at least 50, sets of probes for hybridising a polymorphism selected from Table 3, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprises a probe(s) for hybridising one or more or all of the polymorphisms provided in Table 19, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the kit comprising a probe(s) for hybridising one or more or all of the polymorphisms provided in in Table 22, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

Primers and probes for other polymorphisms can be included with the above exemplified kits. For example, primers and/or probes may be included for detecting a Coronavirus, such as a SARS-CoV-2 viral, infection.

EXAMPLES Example 1—Polymorphisms Associated with Disease Severity in Covid-19 Infected Patients

Approximately 11 million SNP results were analysed. These were sorted by p-value, from lowest to highest and the top one million of these were utilised for further pruning. This equated to all variants p<0.0969. A p-value threshold of p<0.001 was then applied, as was a beta value window between −1 to 1 and an average pooled allele frequency of 0.01-0.99.

These were then further pruned for linkage disequilibrium using the online tool LDLink, snpclip (ldlink.nci.nih.gov) using the EUR populations as reference, set to threshold at R2 of <0.5. Non-single nucleotide variants were excluded if no linked surrogate/proxy SNP was available.

Informative polymorphisms derived from publicly available pooled genome-wide association study (GWAS) results from 716 cases (confirmed COVID-19 (severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) diagnosis and hospitalised) and 616 controls (confirmed COVID-19 diagnosis and non-hospitalised are provided in Table 2.

Informative polymorphisms derived from 2,863 patients within the UK Biobank Study, of which 825 were hospitalized for severe response to the infection. GWAS results were sorted by p-value. A p-value threshold of p<0.00001 was applied, as was an allele frequency threshold set at a minor allele frequency >0.01. The identified polymorphisms are provided in Table 8.

TABLE 8 Informative polymorphisms derived from 2,863 patients within the UK Biobank Study. Chromo- Frequency 1 Frequency 2 p-value for some Position SNP ID Allele 1 Allele 2 Allele Allele association OR 1 3680362 rs146866117 T C 0.00042 0.99958 3.21516E−05 11.3818 1 10993680 rs75721992 C T 0.00091 0.99909 8.03153E−09 18.9971 1 15698556 rs12562412 G C 0.18020 0.81980 7.34872E−05 1.43641 1 15758944 rs117338853 A G 0.00125 0.99875 9.31685E−05 9.83042 1 16109212 rs72647169 G C 0.03574 0.96426 1.72286E−05 4.49562 1 17295659 rs199765517 T C 0.00045 0.99955 6.47434E−05 10.3168 1 20072025 rs199727655 A G 0.00003 0.99997 3.41489E−05 11.3655 1 22538788 rs78360109 A G 0.00727 0.99273 1.32276E−06 3.74574 1 34829829 rs79955780 G A 0.03884 0.96116 8.74145E−05 1.8346 1 38661814 rs61778695 C A 0.03148 0.96852 4.43093E−05 1.93677 1 67804320 rs578200723 GTTA G 0.00168 0.99832 0.000020482 5.75441 1 72488455 rs116544454 T G 0.01396 0.98604 1.85829E−05 2.48638 1 109823418 rs144022094 T C 0.00064 0.99936 3.57493E−05 6.50582 1 109933450 rs56072034 A G 0.02556 0.97444 4.12342E−05 2.10457 1 168696733 rs76129265 T G 0.05740 0.94260 7.51539E−05 1.68733 1 204092087 rs201772428 A G 0.00036 0.99964 4.3011E−06 14.8225 1 206776460 rs35252702 A G 0.00679 0.99321 1.56671E−07 8.81138 1 207610967 rs61821114 T C 0.01504 0.98496 3.00757E−05 2.42913 1 210901242 rs1934624 A T 0.02193 0.97807 5.48665E−05 2.16618 1 218938774 rs76354174 A G 0.02281 0.97719 7.54499E−05 2.10557 1 230907829 rs143186556 A G 0.00089 0.99911 5.46536E−05 7.7448 1 231133014 rs200114138 A G 0.00101 0.99899 3.96515E−06 8.13897 2 22958939 rs59447738 G T 0.10234 0.89766 8.21388E−05 1.5646 2 23005876 rs73918088 A C 0.05530 0.94470 7.50922E−05 1.7204 2 25861939 rs189303418 T C 0.00156 0.99844 0.000040202 5.43754 2 34108876 rs1718746 C G 0.15379 0.84621 8.52475E−05 0.678928 2 34119632 rs1705143 A G 0.15212 0.84788 8.08594E−05 0.676255 2 64630299 rs872241 G A 0.35970 0.64030 7.17367E−05 1.37043 2 64641736 rs11676644 C T 0.30746 0.69254 0.000032141 1.39753 2 115258481 rs56735442 A G 0.02150 0.97850 3.59163E−05 2.25221 2 122952399 rs75945051 G A 0.01208 0.98792 4.38486E−05 2.48027 2 126726522 rs76187206 C G 0.00948 0.99052 5.74668E−05 2.79499 2 160721407 chr2 A AC 0.00001 0.99999 4.45501E−05 10.9923 160721407 2 179428061 rs11896637 T C 0.00304 0.99696 1.81846E−06 11.2684 2 179441917 rs72646881 C T 0.00324 0.99676 1.59099E−05 8.89845 2 179612315 rs145581345 C T 0.00135 0.99865 8.45307E−05 5.91193 2 181910717 rs78593095 A G 0.01388 0.98612 2.26398E−05 2.66342 2 191278341 rs6725814 G A 0.37811 0.62189 1.88302E−05 0.714396 3 2748191 rs80225140 G A 0.03080 0.96920 8.74289E−06 2.11655 3 34944013 rs17032477 G A 0.01700 0.98300 3.63638E−05 2.54994 3 65100060 rs2128405 C A 0.07271 0.92729 1.71219E−05 1.69956 3 70783491 rs6766000 T C 0.13852 0.86148 2.00031E−05 1.53315 3 81810551 rs35196441 T G 0.00100 0.99900 3.53506E−05 11.2502 3 154812638 rs2196521 G A 0.12811 0.87189 3.73634E−05 0.662498 3 160379672 rs4679910 G A 0.38677 0.61323 1.90222E−05 1.4334 3 171853417 rs73167212 G A 0.11289 0.88711 0.00002702 1.5595 3 177796194 rs74911757 T C 0.01229 0.98771 1.43291E−05 2.6372 4 39943691 rs115509062 G C 0.03178 0.96822 6.54964E−05 1.90376 4 45117625 rs75072424 A G 0.11765 0.88235 2.80438E−05 1.54717 4 69795670 rs144454074 A G 0.00307 0.99693 4.19779E−05 4.35549 4 73555556 rs28616128 G A 0.06206 0.93794 3.02015E−06 1.78187 4 75191300 rs115044024 C T 0.01950 0.98050 4.15557E−05 2.18747 4 84216649 rs144185023 A G 0.00166 0.99834 3.72469E−05 5.50931 4 87939942 rs76456240 C T 0.02364 0.97636 9.48747E−05 2.09846 4 121958473 rs202221151 C T 0.00031 0.99969 1.61977E−06 16.7318 4 142326546 rs76589765 G A 0.01836 0.98164 4.20983E−06 2.39938 4 151724769 rs116015734 T C 0.01283 0.98717 9.26679E−05 2.44853 4 153821201 rs62319956 C A 0.00963 0.99037 2.01266E−06 3.02949 4 170238293 rs76519323 A C 0.03375 0.96625 2.30315E−05 1.98212 4 170498270 rs74557505 C T 0.03048 0.96952 6.57926E−05 2.01122 4 181162752 rs17069033 T G 0.00223 0.99777 1.35337E−05 9.07875 4 185567903 rs4647611 C G 0.00897 0.99103 2.72675E−05 6.07186 5 6826973 rs275444 C G 0.12706 0.87294 3.53004E−05 0.530803 5 19993490 rs4466171 A T 0.02086 0.97914 3.08251E−06 5.43862 5 43280480 rs55770078 A G 0.00266 0.99734 1.16965E−05 4.82309 5 99815379 rs115319054 A C 0.01396 0.98604 8.88589E−05 2.37898 5 133519273 rs79601653 G A 0.05445 0.94555 9.89897E−05 1.68817 5 155405405 rs958444 T C 0.04320 0.95680 2.01272E−05 0.540169 5 160448591 rs11749317 C T 0.01465 0.98535 0.000080983 2.31323 6 2885791 rs318470 C A 0.03985 0.96015 9.76222E−05 0.534104 6 16217762 rs149442766 A G 0.00598 0.99402 3.42524E−05 3.20428 6 26408145 rs144114619 A T 0.00298 0.99702 4.57681E−05 4.32309 6 33156845 rs41268014 G C 0.00111 0.99889 1.61406E−05 7.06085 6 36976747 rs140572234 C G 0.00360 0.99640 6.20165E−08 4.94704 6 36999980 rs114925152 G C 0.01045 0.98955 0.000019845 2.8117 6 37139030 rs35760989 C G 0.00316 0.99684 2.15792E−05 4.19015 6 101279459 rs9485415 T C 0.05955 0.94045 2.95345E−05 1.79563 6 147885588 rs140643252 A G 0.00080 0.99920 0.000080315 7.36219 7 21730647 rs7790948 G T 0.14286 0.85714 3.13489E−05 1.51436 7 35720134 rs79496619 T G 0.01734 0.98266 3.21712E−05 4.65324 7 38677134 rs78966608 A C 0.06041 0.93959 7.06676E−05 1.69619 7 100483400 rs200675508 A G 0.00165 0.99835 7.25535E−05 5.06219 7 104782689 rs55743527 G T 0.00060 0.99940 1.36022E−06 11.6143 7 122122078 rs73431600 A G 0.01909 0.98091 5.15467E−05 2.32846 7 122196872 rs73433754 A C 0.01639 0.98361 3.48011E−05 2.47199 7 154926067 rs117164958 C G 0.01908 0.98092 7.61301E−06 4.84386 7 158928541 rs13225056 A G 0.02825 0.97175 1.53596E−05 2.04944 8 2044114 rs147776183 T C 0.00025 0.99975 8.64871E−06 13.5674 8 9478274 rs78876374 T C 0.05327 0.94673 8.85355E−05 1.69943 8 19218826 rs145746072 A G 0.00063 0.99937 1.91735E−05 5.15347 8 20354600 rs13249119 G A 0.12835 0.87165 1.33633E−05 1.57795 8 20447019 rs73626732 A G 0.08816 0.91184 1.40229E−05 1.65522 8 20460661 rs35258926 G T 0.08444 0.91556 9.04111E−06 1.68733 8 39799765 rs7845003 T C 0.38763 0.61237 9.39614E−05 1.37383 8 125839433 rs118040942 T C 0.02098 0.97902 0.000059504 2.11631 8 130677813 rs57557483 A G 0.03911 0.96089 8.17573E−06 1.95465 8 130694201 rs75572486 A G 0.03949 0.96051 2.53314E−05 1.8823 9 10276812 rs10959000 A G 0.06996 0.93004 7.85152E−05 1.65945 9 22080363 rs16905613 G A 0.00861 0.99139 5.78633E−06 3.06828 9 38669062 rs2993177 A G 0.02733 0.97267 7.37838E−05 0.512648 9 79453107 rs7853555 T C 0.39843 0.60157 5.25711E−05 0.726342 9 83032179 rs72744937 G A 0.01117 0.98883 6.92799E−05 2.60258 9 100137855 rs200908751 A G 0.00074 0.99926 6.57652E−05 7.53882 9 101874496 rs76094400 A G 0.01217 0.98783 3.95481E−08 3.22873 9 125704675 rs76670825 A G 0.01316 0.98684 5.01185E−05 2.47436 9 125903047 rs77089732 A G 0.01284 0.98716 0.00005145 2.45902 9 128794405 rs62570501 T C 0.05704 0.94296 7.35517E−05 1.70643 9 131771551 rs17455482 A G 0.00240 0.99760 5.2118E−06 5.14273 10 67680203 rs41313840 G A 0.00029 0.99971 7.19977E−08 11.5573 10 127258691 rs7084502 A G 0.41534 0.58466 7.81329E−05 0.720431 10 131456440 rs79858702 T C 0.00885 0.99115 0.000070971 2.83622 11 1795214 rs79194907 G A 0.08673 0.91327 6.83674E−05 0.457071 11 5146083 rs12365149 T C 0.08312 0.91688 3.11673E−05 0.427618 11 44547746 rs12275504 G T 0.02803 0.97197 1.40655E−05 2.13659 11 46727333 rs149066130 A G 0.00042 0.99958 7.98651E−05 10.0401 11 57982229 rs1966836 A G 0.29483 0.70517 6.87681E−05 0.720184 11 65414949 rs199717374 T C 0.00021 0.99979 1.05822E−05 9.34606 11 78904266 rs75970706 T C 0.03718 0.96282 0.000040305 1.8614 11 94665002 rs76360689 A G 0.00749 0.99251 1.05432E−06 3.44965 11 133713033 rs75786498 A G 0.01903 0.98097 3.13102E−05 2.20737 12 2174748 rs117821007 C T 0.01160 0.98840 4.86991E−05 2.65265 12 25681286 rs58907459 T C 0.12688 0.87312 6.07118E−05 1.50994 12 57589676 rs143285614 A G 0.00041 0.99959 2.69071E−05 11.6706 12 125302200 rs143093152 G A 0.00050 0.99950 6.62377E−06 14.0613 13 39433574 rs138539682 A G 0.00073 0.99927 3.32149E−05 8.17464 13 68302925 rs75022796 T C 0.00560 0.99440 7.24669E−05 3.17997 14 52276643 rs117852779 C T 0.02739 0.97261 2.89233E−06 2.17106 14 80405359 rs11159425 T G 0.11363 0.88637 4.36025E−07 0.583 14 80570671 rs114463019 G T 0.01076 0.98924 3.85403E−05 2.71426 14 87813714 rs28450466 A G 0.37204 0.62796 9.45754E−05 1.35996 14 93016441 rs57851052 C T 0.32996 0.67004 5.53318E−05 1.37778 14 104863663 rs80083325 A G 0.05367 0.94633 0.000073746 1.71423 15 22845849 rs150408740 G A 0.00068 0.99932 1.76793E−05 8.79206 15 34498314 rs75915717 T C 0.08459 0.91541 1.0293E−06 1.74564 15 41047777 rs35673728 C T 0.06056 0.93944 6.67895E−05 1.67388 15 41254865 rs12915860 C A 0.06743 0.93257 1.01336E−05 1.72191 15 41712936 rs62001419 A C 0.05640 0.94360 9.84979E−05 1.68361 15 52689631 rs1724577 T G 0.00955 0.99045 2.42041E−05 0.197849 15 58047086 rs77910305 G C 0.00656 0.99344 1.30369E−05 3.14637 15 65851028 rs200531541 A G 0.00017 0.99983 3.83063E−06 13.9416 15 78471034 rs34921279 T C 0.00117 0.99883 2.40367E−05 8.48175 15 84063245 rs12591031 G A 0.21734 0.78266 1.16687E−05 1.46192 15 91452594 rs142925505 T C 0.00205 0.99795 6.04339E−07 5.95363 16 49391921 rs62029091 A G 0.11884 0.88116 7.11949E−05 1.52258 16 49394276 rs8057939 C T 0.12559 0.87441 1.12522E−05 1.57483 16 60671279 rs118097562 T A 0.00770 0.99230 1.90641E−05 6.63236 16 61851413 rs151208133 T C 0.00057 0.99943 6.3915E−06 9.86885 16 81194912 rs11642802 C A 0.26295 0.73705 4.32104E−06 1.45947 16 90075827 rs201800670 T C 0.00044 0.99956 1.60067E−05 12.5559 17 1462712 rs73298816 G A 0.29300 0.70700 5.44207E−05 0.682825 17 3844344 rs144535413 T C 0.00487 0.99513 1.11488E−05 3.62812 17 36485146 rs147966258 A G 0.00198 0.99802 1.64933E−05 5.06987 17 39240563 rs193005959 A C 0.00837 0.99163 3.72836E−05 2.98372 17 55803083 rs72841559 C G 0.01897 0.98103 2.26086E−05 2.2379 17 56329775 rs368901060 ACCAT A 0.00219 0.99781 4.70507E−06 5.18629 17 63919929 rs7220318 G A 0.00461 0.99539 2.39945E−05 10.9127 17 72890474 rs689992 T A 0.26539 0.73461 8.97907E−05 1.39123 17 78215658 rs117140258 A G 0.02691 0.97309 4.15567E−05 2.08397 18 4610215 rs76902871 G T 0.01267 0.98733 2.95635E−05 2.56921 18 13501162 rs2298530 C T 0.03718 0.96282 6.48889E−05 1.85512 18 14310187 rs117505121 G A 0.01057 0.98943 3.59448E−05 2.71447 18 49288587 rs117781678 A C 0.01002 0.98998 4.32852E−05 2.66495 18 76650871 rs7240086 G A 0.43336 0.56664 6.95487E−06 1.42281 19 36018109 rs74726174 C T 0.00069 0.99931 1.82372E−05 12.2885 19 38867031 rs200403794 A G 0.00019 0.99981 8.36021E−05 10.0252 20 8782776 rs138434221 A C 0.00031 0.99969 4.80296E−06 14.6291 20 15632993 rs6110707 C T 0.17779 0.82221 1.31264E−05 1.50802 20 55021575 rs6069749 T C 0.07845 0.92155 2.92257E−05 1.64645 20 55111747 rs6014757 A G 0.15160 0.84840 6.44584E−05 1.47163 21 19045795 rs73200561 A T 0.00879 0.99121 9.40896E−05 2.714 21 37444937 rs2230191 A G 0.00079 0.99921 3.4537E−07 13.3335 22 28016883 rs1885362 A C 0.15733 0.84267 4.65803E−05 1.70351 22 40056937 rs113038998 T C 0.06752 0.93248 1.37758E−05 1.73209 22 44285118 rs117421847 A G 0.01292 0.98708 6.60782E−05 2.4314

Example 2—Genetic Risk Assessment—108 Polymorphism Panel

SNP-based (relative) risk score was calculated using estimates of the odds ratio (OR) per allele and risk allele frequency (p) assuming independent and additive risks on the log OR scale. For each SNP, the unscaled population average risk was calculated as μ=(1−p)2+2p(1−p) OR+p2OR2. Adjusted risk values (with a population average risk equal to 1 were calculated as 1/μ, OR/μ and OR2/μ for the three genotypes defined by number of risk alleles (0, 1, or 2). The overall SNP-based risk score was then calculated by multiplying the adjusted risk values for each of the 108 SNPs (Tables 9 and 10).

Thus, a polygenic risk score can discriminate between patients with a confirmed Covid-19 infection who developed a severe response to that infection, requiring hospitalization, and those who did not require hospitalization.

Example 3—Genetic Risk Assessment—58 Polymorphism Panel

The present inventors have found that a polygenic risk score can discriminate between patients with a confirmed Covid-19 infection who developed a severe response to that infection, requiring hospitalization, and those who did not require hospitalization.

The model has been developed using 2,863 patients within the UK Biobank Study, of which 825 were hospitalized for severe response to the infection.

SNP-based (relative) risk score was calculated using estimates of the odds ratio (OR) per allele and risk allele frequency (p) assuming independent and additive risks on the log OR scale. For each SNP, the unscaled population average risk was calculated as μ=(1−p)2+2p(1−p) OR+p2OR2. Adjusted risk values (with a population average risk equal to 1 were calculated as 1/μ, OR/μ and OR2/μ for the three genotypes defined by number of risk alleles (0, 1, or 2). The overall SNP-based risk score was then calculated by multiplying the adjusted risk values for each of the 58 SNPs (Table 11). The 58 SNPs analysed are provided in Table 3.

Thus, a polygenic risk score can discriminate between patients with a confirmed Covid-19 infection who developed a severe response to that infection, requiring hospitalization, and those who did not require hospitalization. Due to the higher OR, this panel performed better than the 108 SNP panel described in Example 2.

TABLE 9 Informative polymorphisms used in genetic risk assessment of Example 2. Frequency Frequency p-value for Chromosome Position SNP ID Allele 1 Allele 2 Allele 1 Allele 2 association OR 1 15698556 rs12562412 G C 0.180202869 0.819797131 7.34872E−05 1.43641 1 16109212 rs72647169 G C 0.035742611 0.964257389 1.72286E−05 4.49562 1 34829829 rs79955780 G A 0.038842122 0.961157878 8.74145E−05 1.8346 1 38661814 rs61778695 C A 0.031483826 0.968516174 4.43093E−05 1.93677 1 72488455 rs116544454 T G 0.013959529 0.986040471 1.85829E−05 2.48638 1 109933450 rs56072034 A G 0.02556272 0.97443728 4.12342E−05 2.10457 1 168696733 rs76129265 T G 0.05740251 0.94259749 7.51539E−05 1.68733 1 207610967 rs61821114 T C 0.015043834 0.984956166 3.00757E−05 2.42913 1 210901242 rs1934624 A T 0.021933325 0.978066675 5.48665E−05 2.16618 1 218938774 rs76354174 A G 0.022811438 0.977188562 7.54499E−05 2.10557 2 22958939 rs59447738 G T 0.102337052 0.897662948 8.21388E−05 1.5646 2 23005876 rs73918088 A C 0.055296476 0.944703524 7.50922E−05 1.7204 2 34108876 rs1718746 C G 0.153787681 0.846212319 8.52475E−05 0.678928 2 34119632 rs1705143 A G 0.152124807 0.847875193 8.08594E−05 0.676255 2 64630299 rs872241 G A 0.359695861 0.640304139 7.17367E−05 1.37043 2 64641736 rs11676644 C T 0.30746047 0.69253953 0.000032141 1.39753 2 115258481 rs56735442 A G 0.021502475 0.978497525 3.59163E−05 2.25221 2 122952399 rs75945051 G A 0.012078152 0.987921848 4.38486E−05 2.48027 2 181910717 rs78593095 A G 0.013875458 0.986124542 2.26398E−05 2.66342 2 191278341 rs6725814 G A 0.378106477 0.621893523 1.88302E−05 0.714396 3 2748191 rs80225140 G A 0.030803699 0.969196301 8.74289E−06 2.11655 3 34944013 rs17032477 G A 0.017000358 0.982999642 3.63638E−05 2.54994 3 65100060 rs2128405 C A 0.072712034 0.927287966 1.71219E−05 1.69956 3 70783491 rs6766000 T C 0.138522268 0.861477732 2.00031E−05 1.53315 3 81810551 rs2196521 G A 0.128112119 0.871887881 3.73634E−05 0.662498 3 160379672 rs4679910 G A 0.386771125 0.613228875 1.90222E−05 1.4334 3 171853417 rs73167212 G A 0.112891842 0.887108158 0.00002702  1.5595 3 177796194 rs74911757 T C 0.012287422 0.987712578 1.43291E−05 2.6372 4 39943691 rs115509062 G C 0.031782343 0.968217657 6.54964E−05 1.90376 4 45117625 rs75072424 A G 0.117654783 0.882345217 2.80438E−05 1.54717 4 73555556 rs28616128 G A 0.06205979 0.93794021 3.02015E−06 1.78187 4 75191300 rs115044024 C T 0.019501076 0.980498924 4.15557E−05 2.18747 4 87939942 rs76456240 C T 0.02364031 0.97635969 9.48747E−05 2.09846 4 142326546 rs76589765 G A 0.018356247 0.981643753 4.20983E−06 2.39938 4 151724769 rs116015734 T C 0.012831113 0.987168887 9.26679E−05 2.44853 4 170238293 rs76519323 A C 0.033750916 0.966249084 2.30315E−05 1.98212 4 170498270 rs74557505 C T 0.030475432 0.969524568 6.57926E−05 2.01122 5 6826973 rs275444 C G 0.127064744 0.872935256 3.53004E−05 0.530803 5 19993490 rs4466171 A T 0.020864374 0.979135626 3.08251E−06 5.43862 5 99815379 rs115319054 A C 0.013956451 0.986043549 8.88589E−05 2.37898 5 133519273 rs79601653 G A 0.054445035 0.945554965 9.89897E−05 1.68817 5 155405405 rs958444 T C 0.043203962 0.956796038 2.01272E−05 0.540169 5 160448591 rs11749317 C T 0.014652992 0.985347008 0.000080983 2.31323 6 2885791 rs318470 C A 0.039851542 0.960148458 9.76222E−05 0.534104 6 36999980 rs114925152 G C 0.010452207 0.989547793 0.000019845 2.8117 6 101279459 rs9485415 T C 0.059550603 0.940449397 2.95345E−05 1.79563 7 21730647 rs7790948 G T 0.142864617 0.857135383 3.13489E−05 1.51436 7 35720134 rs79496619 T G 0.01734375 0.98265625 3.21712E−05 4.65324 7 38677134 rs78966608 A C 0.060412303 0.939587697 7.06676E−05 1.69619 7 122122078 rs73431600 A G 0.019090743 0.980909257 5.15467E−05 2.32846 7 122196872 rs73433754 A C 0.016394855 0.983605145 3.48011E−05 2.47199 7 154926067 rs117164958 C G 0.019077785 0.980922215 7.61301E−06 4.84386 7 158928541 rs13225056 A G 0.028246298 0.971753702 1.53596E−05 2.04944 8 9478274 rs78876374 T C 0.053269431 0.946730569 8.85355E−05 1.69943 8 20354600 rs13249119 G A 0.12835319 0.87164681 1.33633E−05 1.57795 8 20447019 rs73626732 A G 0.088162098 0.911837902 1.40229E−05 1.65522 8 20460661 rs35258926 G T 0.084440378 0.915559622 9.04111E−06 1.68733 8 39799765 rs7845003 T C 0.387630529 0.612369471 9.39614E−05 1.37383 8 125839433 rs118040942 T C 0.020977249 0.979022751 0.000059504 2.11631 8 130677813 rs57557483 A G 0.039111916 0.960888084 8.17573E−06 1.95465 8 130694201 rs75572486 A G 0.039487371 0.960512629 2.53314E−05 1.8823 9 10276812 rs10959000 A G 0.069955622 0.930044378 7.85152E−05 1.65945 9 38669062 rs2993177 A G 0.027325082 0.972674918 7.37838E−05 0.512648 9 79453107 rs7853555 T C 0.398429245 0.601570755 5.25711E−05 0.726342 9 83032179 rs72744937 G A 0.011167213 0.988832787 6.92799E−05 2.60258 9 101874496 rs76094400 A G 0.012165395 0.987834605 3.95481E−08 3.22873 9 125704675 rs76670825 A G 0.013160405 0.986839595 5.01185E−05 2.47436 9 125903047 rs77089732 A G 0.012843423 0.987156577 0.00005145  2.45902 9 128794405 rs62570501 T C 0.057035262 0.942964738 7.35517E−05 1.70643 10 127258691 rs7084502 A G 0.415343172 0.584656828 7.81329E−05 0.720431 11 1795214 rs79194907 G A 0.086733113 0.913266887 6.83674E−05 0.457071 11 5146083 rs12365149 T C 0.083117054 0.916882946 3.11673E−05 0.427618 11 44547746 rs12275504 G T 0.028028822 0.971971178 1.40655E−05 2.13659 11 57982229 rs1966836 A G 0.294826316 0.705173684 6.87681E−05 0.720184 11 78904266 rs75970706 T C 0.037184377 0.962815623 0.000040305 1.8614 11 133713033 rs75786498 A G 0.019027141 0.980972859 3.13102E−05 2.20737 12 2174748 rs117821007 C T 0.011596011 0.988403989 4.86991E−05 2.65265 12 25681286 rs58907459 T C 0.126878043 0.873121957 6.07118E−05 1.50994 14 52276643 rs117852779 C T 0.027392806 0.972607194 2.89233E−06 2.17106 14 80405359 rs11159425 T G 0.113626338 0.886373662 4.36025E−07 0.583 14 80570671 rs114463019 G T 0.010759957 0.989240043 3.85403E−05 2.71426 14 87813714 rs28450466 A G 0.372042781 0.627957219 9.45754E−05 1.35996 14 93016441 rs57851052 C T 0.329959028 0.670040972 5.53318E−05 1.37778 14 104863663 rs80083325 A G 0.053669505 0.946330495 0.000073746 1.71423 15 34498314 rs75915717 T C 0.084593227 0.915406773  1.0293E−06 1.74564 15 41047777 rs35673728 C T 0.060558997 0.939441003 6.67895E−05 1.67388 15 41254865 rs12915860 C A 0.06742592 0.93257408 1.01336E−05 1.72191 15 41712936 rs62001419 A C 0.056399246 0.943600754 9.84979E−05 1.68361 15 84063245 rs12591031 G A 0.217339031 0.782660969 1.16687E−05 1.46192 16 49391921 rs62029091 A G 0.118836542 0.881163458 7.11949E−05 1.52258 16 49394276 rs8057939 C T 0.125591649 0.874408351 1.12522E−05 1.57483 16 81194912 rs11642802 C A 0.262949597 0.737050403 4.32104E−06 1.45947 17 1462712 rs73298816 G A 0.292998283 0.707001717 5.44207E−05 0.682825 17 55803083 rs72841559 C G 0.018970721 0.981029279 2.26086E−05 2.2379 17 72890474 rs689992 T A 0.265385949 0.734614051 8.97907E−05 1.39123 17 78215658 rs117140258 A G 0.026907587 0.973092413 4.15567E−05 2.08397 18 4610215 rs76902871 G T 0.012668006 0.987331994 2.95635E−05 2.56921 18 13501162 rs2298530 C T 0.03717617 0.96282383 6.48889E−05 1.85512 18 14310187 rs117505121 G A 0.010569152 0.989430848 3.59448E−05 2.71447 18 49288587 rs117781678 A C 0.010023409 0.989976591 4.32852E−05 2.66495 18 76650871 rs7240086 G A 0.433358842 0.566641158 6.95487E−06 1.42281 20 15632993 rs6110707 C T 0.177787033 0.822212967 1.31264E−05 1.50802 20 55021575 rs6069749 T C 0.078451567 0.921548433 2.92257E−05 1.64645 20 55111747 rs6014757 A G 0.15160471 0.84839529 6.44584E−05 1.47163 22 28016883 rs1885362 A C 0.157331933 0.842668067 4.65803E−05 1.70351 22 40056937 rs113038998 T C 0.067518225 0.932481775 1.37758E−05 1.73209 22 44285118 rs117421847 A G 0.012916257 0.987083743 6.60782E−05 2.4314

Example 4—Combining Genetic and Clinical Risk Assessment

The present specification provides methods for a Covid-19 risk model which combines a clinical risk assessment and a genetic risk assessment which can be used discriminate between cases with a severe response to Covid-19 infection, versus controls without a severe response.

The clinical risk factors incorporated into a combined model are assigned a relative risk, which indicates the magnitude of association with the severity of a Covid-19 infection, the clinical factors are combined with the polygenic risk score by multiplication. For example clinical risk factor A is assigned the relative risk RRa and clinical risk factor B is assigned the relative risk RRb. The full risk score is then calculated as Polygenic Risk Score×RRa×RRb=Combined Risk.

TABLE 10 Performance characteristics of a 108 SNP polygenic risk score to discriminate between cases with a severe response to Covid-19 infection, versus controls without a severe response. Number of SNPs in the Z- Area polygenic Odds Standard sta- 95% Conf. of risk score Ratio Error tistic P > z interval ROC 108 1.57 0.109307 6.51 0.00 1.371796-1.801599 0.66

TABLE 11 Performance characteristics of a 58 SNP polygenic risk score to discriminate between cases with a severe response to Covid-19 infection, versus controls without a severe response. Number of SNPs in the Z- Area polygenic Odds Standard sta- 95% Conf. of risk score Ratio Error tistic P > z interval ROC 58 5.49 0.6401827 14.61 0.00 4.369035-6.900403 0.87

Example 5—Combined Genetic and Clinical Risk Assessment—64 Polymorphism Panel Data and Eligibility

The inventors extracted COVID-19 testing and hospital records from the UK Biobank COVID-19 data portal on 15 Sep. 2020. At the time of data extraction, primary care data was only available for just over half of the identified participants and was therefore not used in these analyses.

Eligible participants were those who had tested positive for COVID-19 and for whom SNP genotyping data and linked hospital records were available. Of the 18,221 participants with COVID-19 test results, 1,713 had tested positive and 1,582 of those had both SNP and hospital data available.

COVID-19 Severity

The inventors used source of test result as a proxy for severity of disease: outpatient representing non-severe disease and inpatient representing severe disease. For participants with multiple test results, the disease was considered to be severe if at least one result came from an inpatient setting.

Selection of SNPs for Risk of Severe COVID-19

The inventors identified 62 SNPs from the publicly available (release 2) results of the meta-analysis of non-hospitalised versus hospitalised cases of COVID-19 conducted by the COVID-19 Host Genetics Initiative consortium (COVID-19 Host Genetics Initiative (2020) and COVID-19 Host Genetics Initiative: results. 2020 accessed May 13, 2020, at www.covid19hg.org/results). ( ). P<0.0001 was used as the threshold for loci selection and variants that were associated with hospitalisation in only one of the five studies included in the meta-analysis were removed. Variants that had a minor allele frequency of <0.01 and beta coefficients from −1 to 1 were then discarded (Dayem et al., 2018). Linkage disequilibrium pruning was performed using an r² threshold of 0.5 against the 1000 Genomes European populations (CEU, TSI, FIN, GBR, IBS) representing the ethnicities of the submitted populations (Machiela et al., 2015). Where possible, SNP variants were chosen over insertion—deletion variants to facilitate laboratory validation testing.

The two lead SNPs from the loci found by Ellinghaus et al. (2020) that reached genome-wide significance were also included. Therefore, a panel of 64 SNPs for severe COVID-19 was used.

Genetic Risk Score

For the SNPs identified from the COVID-19 Host Genetics Initiative, the odds ratios for severe disease ranged from 1.5 to 2.7 (Table 4). While the inventors would normally construct a SNP relative risk score by using published odds ratios and allele frequencies to calculate adjusted risk values (with a population average of 1) for each SNP and then multiplying the risks for each SNP (Mealiffe et al., 2020), the size of the odds ratios for each SNP meant that this approach could result in relative risk SNP scores of several orders of magnitude. Therefore, to construct the SNP score for this study, the inventors calculated the percentage of risk alleles present in the genotyped SNPs for each participant as generally described in WO 2005/086770. More specifically, for each of the 64 SNPs, if the subject was homozygous for the risk allele they were scored as 2, if they were heterozygous for the risk allele they were scored as 1, and if they we homozygous for the risk allele they were scored as 0. The total number was then converted to a percentage for use in determining risk.

Percentage rather than a count was used because some of the eligible participants had missing data for some SNPs (9% had all SNPs genotyped, 82% were missing 1-5 SNPs and 9% were missing 6-15 SNPs).

Imputation of ABO Genotype

Blood type was imputed for genotyped UK Biobank participants using three SNPs (rs505922, rs8176719 and rs8176746) in the ABO gene on chromosome 9q34.2. A rs8176719 deletion (or for those with no result for rs8176719, a T allele at rs505922) was considered to indicate haplotype O. At rs8176746, haplotype A was indicated by the presence of the G allele and haplotype B was indicated by the presence of the T allele (Melzer et al., 2008; Wolpin et al., 2010).

Clinical Risk Factors

Risk factors for severe COVID-19 were identified from large epidemiological studies of electronic health records (Williamson et al., 2020; Petrilli et al., 2020) and advice posted on the Centers for Disease Control and Prevention website. Rare monogenic diseases (thalassemia, cystic fibrosis and sickle cell disease) were not considered in these analyses.

Age was classified as 50-59 years, 60-69 year and 70+ years. This was based on the participants' approximate age at the peak of the first wave of infections (April 2020) and was calculated using the participants' month and year of birth. Self-reported ethnicity was classified as white and other (including unknown). The Townsend deprivation score at baseline was classified into quintiles defined by the distribution of the score in the UK Biobank as a whole. Body mass index and smoking status were also obtained from the baseline assessment data. Body mass index was inverse transformed and then rescaled by multiplying by 10. Smoking status was defined as current versus past, never or unknown. The other clinical risk factors were extracted from hospital records by selecting records with ICD9 or ICD10 codes for the disease of interest.

Statistical Methods

Logistic regression was used to examine the association of risk factors with severity of COVID-19 disease. To develop the final model, the inventors began with a base model that included SNP score, age group and gender. They then included all of the candidate variables and used step-wise backwards selection to remove variables with p-values of >0.05. The final model was refined by considering the addition of the removed candidate variables one at a time. Model selection was informed by examination of the Akaike information criterion and the Bayesian information criterion, with a decrease of >2 indicating a statistically significant improvement.

Model calibration was assessed using the Pearson-Windmeijer goodness-of-fit test and model discrimination was measured using the area under the receiver operating characteristic curve (AUC). To compare the effect sizes of the variables in the final model, the inventors used the odds per adjusted standard deviation (Hopper, 2015) using dummy variables for age group and ABO blood type. The intercept and beta coefficients from the final model were used to calculate the COVID-19 risk score for all UK Biobank participants.

Stata (version 16.1) (StataCorp LLC: College Station, Tex., USA) was used for analyses; all statistical tests were two-sided, and p-values of less than 0.05 were considered nominally statistically significant.

Results

Of the 1,582 UK Biobank participants with a positive SARS-CoV-2 test result and hospital and SNP data available, 564 (35.7%) were from an outpatient setting and considered not to have severe disease (controls), while 1,018 (64.4%) were from an inpatient setting and considered to have severe disease (cases). Cases ranged in age from 51 to 82 years with a mean of 69.1 (standard deviation [SD]=8.8) years. Controls ranged in age from 50 to 82 years with a mean of 65.0 (SD=9.0) years. Mean body mass index was 29.0 kg/m2 (SD=5.4) for cases and 28.5 (SD=5.4) for controls. Body mass index was transformed to the inverse multiplied by 10 for all analyses and ranged from 0.2 to 0.6 for both cases and controls. The percentage of risk alleles in the SNP score ranged from 47.6 to 73.8 for cases and from 43.7 to 72.5 for controls. The distributions of the variables of interest for cases and controls and the unadjusted odd ratios and 95% confidence intervals (CI) are shown in Table 12.

The model selected included SNP score, age group, gender, ethnicity, ABO blood type, and a history of autoimmune disease (rheumatoid arthritis, lupus or psoriasis), haematological cancer, non-haematological cancer, diabetes, hypertension or respiratory disease (excluding asthma) and was a good fit to the data (Windmeijer's H=0.02, p=0.9) (Table 13). The SNP score was strongly associated with severity of disease, increasing risk by 19% per percentage increase in risk alleles. A negative impact of age was only evident in the group aged 70 years and over, and while gender was not statistically significant (p=0.3), it was retained because it was one of the three variables considered the base model to which other variables were added. Ethnicity showed a 43% increase in risk for non-whites but was only marginally statistically significant (p=0.06). The AB blood type was protective (p=0.007), but the protective effect of blood type A and the increased risk for blood type B were not statistically significant (p=0.1 and p=0.4, respectively).

The SNP score was, by far, the strongest predictor followed by respiratory disease and age 70 years or older.

The receiver operating characteristic curves for the final model and for alternative models with clinical factors only; SNP score only; and age and gender are shown in FIG. 1. The SNP score alone had an AUC of 0.680 (95% CI=0.652, 0.708). The model with age and gender had an AUC of 0.635 (95% CI=0.607, 0.662), while the model with clinical factors only had an AUC of 0.723 (95% CI=0.698, 0.749). Given that the minimum possible value for an AUC is 0.5, the model with clinical factors only was a 65% improvement over the model with age and gender (χ2=57.97, df=1, p<0.001). The combined model had an AUC of 0.786 (95% CI=0.763, 0.808) and was an 28% improvement over the model with clinical factors only (χ2=39.54, df=1, p<0.001) and a 111% improvement over the model with age and sex (χ2=113.67, df=1, p<0.001).

TABLE 12 Characteristics of cases and controls and unadjusted odds ratios for risk of severe COVID-19. Unadjusted 95% confidence Variable Cases Controls odds ratio interval p-value Continuous variables Mean (SD) Mean (SD) SNP score % risk 62.1 (4.1) 59.3 (4.7) 1.16 1.13, 1.19 <0.001 alleles Inverse of body 10/BMI 0.36 (0.06) 0.36 (0.06) 0.15 0.03, 0.79 0.03 mass index (kg/m²) Categorical variables N (%) N (%) Age group 50-59 218 (21.4) 210 (37.2) — (years) 60-60 210 (20.6) 157 (27.8) 1.29 0.97, 1.71 0.08 70+ 590 (58.0) 197 (34.9) 2.89 2.25, 3.70 <0.001 Gender Female 443 (43.5) 298 (52.8) — Male 575 (56.5) 266 (47.2) 1.45 1.18, 1.79 <0.001 Ethnicity White 888 (87.2) 489 (86.7) — Other 123 (12.1) 73 (12.9) 0.93 0.68, 1.26 0.6 Missing 7 (0.7) 2 (0.4) Quintile of 1 134 (13.2) 84 (14.9) — Townsend 2 165 (16.2) 95 (16.8) 1.09 0.75, 1.58 0.7 deprivation 3 179 (17.6) 98 (17.4) 1.14 0.79, 1.65 0.5 index at baseline 4 215 (21.1) 124 (22.0) 1.09 0.77, 1.54 0.6 5 325 (31.9) 162 (28.7) 1.26 0.90, 1.75 0.2 Missing 0 (0.0) 1 (0.2) ABO blood type O 425 (41.8) 235 (41.7) — A 450 (44.2) 249 (44.2) 1.00 0.80, 1.25 1.0 B 113 (11.1) 55 (9.8) 1.14 0.79, 1.63 0.5 AB 30 (3.0) 25 (4.4) 0.66 0.38, 1.15 0.1 Smoking status Never/ 882 (86.6) 499 (88.5) — at baseline previous Current 124 (12.2) 60 (10.6) 1.17 0.84, 1.62 0.3 Missing 12 (1.2) 5 (0.9) Asthma No 852 (83.7) 487 (86.4) — Yes 166 (16.3) 77 (13.7) 1.23 0.92, 1.65 0.2 Autoimmune No 947 (93.0) 547 (97.0) — (rheumatoid/ Yes 71 (7.0) 17 (3.0) 2.41 1.41, 4.14 0.001 lupus/psoriasis) Cancer - No 972 (95.5) 558 (98.9) — haematological Yes 46 (4.5) 6 (1.1) 4.40  1.87, 10.37 0.001 Cancer - non- No 799 (78.5) 486 (86.2) — haematological Yes 219 (21.5) 78 (13.8) 1.71 1.29, 2.26 <0.001 Cerebro- No 847 (83.2) 503 (89.2) — vascular Yes 171 (16.8) 61 (10.8) 1.66 1.22, 2.28 0.001 disease Diabetes No 765 (75.2) 493 (87.4) — Yes 253 (24.9) 71 (12.6) 2.30 1.72, 3.06 <0.001 Heart disease No 633 (62.2) 437 (77.5) — Yes 385 (37.8) 127 (22.5) 2.09 1.66, 2.65 <0.001 Hypertension No 419 (41.2) 354 (62.8) — Yes 599 (58.8) 210 (37.2) 2.41 1.95, 2.98 <0.001 Immuno- No 1,001 (98.3) 560 (99.3) — compromised Yes 17 (1.7) 4 (0.7) 2.38 0.80, 7.10 0.1 Kidney disease No 859 (84.4) 521 (92.4) — Yes 159 (15.6) 43 (7.6) 2.24 1.57, 3.19 <0.001 Liver disease No 937 (92.0) 541 (95.9) — Yes 81 (8.0) 23 (4.1) 2.03 1.26, 3.27 0.003 Respiratory No 571 (56.1) 486 (86.2) — disease Yes 447 (43.9) 78 (13.8) 4.88 3.73, 6.38 <0.001 (excluding asthma)

TABLE 13 Final model for risk of severe COVID-19 given a positive test. β 95% confidence Variable coefficient interval p-value Model intercept −10.7657  −12.5559, −8.9755  <0.001 SNP score % risk 0.1717 0.1429, 0.2006 <0.001 alleles Age group 18-29 −1.3111  (years)* 30-39 −0.8348  40-49 −0.4038  50-59 — 60-69 −0.0600  −0.3819, 0.2619  0.7 70+ 0.5325 0.2213, 0.8438 0.001 Gender Female — Male 0.1387 −0.1005, 0.3779  0.3 Ethnicity White — Other 0.3542 −0.0084, 0.7167  0.06 ABO blood type O — A −0.2164  −0.4726, 0.0397  0.1 B 0.1712 −0.2348, 0.5773  0.4 AB −0.8746  −1.5087, −0.2404 0.007 Autoimmune No — disease Yes 0.7876 0.1832, 1.3920 0.01 (rheumatoid arthritis/ lupus/psoriasis) Cancer - No — haematological Yes 1.0375 0.0994, 1.9756 0.03 Cancer - non- No — haematological Yes 0.3667 0.0401, 0.6933 0.03 Diabetes No — Yes 0.4890 0.1450, 0.8330 0.005 Hypertension No — Yes 0.3034 0.0313, 0.5756 0.03 Respiratory No — disease Yes 1.2331 0.9317, 0.1535 <0.001 (excluding asthma) *Note: The β coefficient is the natural log of the odds ratio; estimates for the 18-29, 30-39 and 40-49 age groups are based on information on page 9 of the Centers for Disease Control COVIDView report for 1 Aug. 2020.

FIG. 2 illustrates the difference in the distributions of the COVID-19 risk scores in cases and controls. The median score was 3.35 for cases and 0.90 for controls. Fifteen percent of cases and 53% of controls had COVID-19 risk scores of less than 1, and 18% of cases and 25% of controls had scores ≥1 and <2. COVID-19 risk scores ≥2 were more common in cases than in controls, with 13% of cases and 9% of controls having scores ≥2 and <3, 8% of cases and 4% of controls having scores ≥3 and <4, and 38% of cases and 6% of controls having scores ≥4.

FIG. 3 shows that the distribution of the COVID-19 risk score in the whole UK Biobank is similar to that for the controls in FIG. 2b . The median risk score in the whole UK Biobank was 1.32. Thirty-eight percent of the UK Biobank have COVID-19 risk scores of less than 1, while 29% have scores ≥1 and <2, 13% have scores ≥2 and <3, 6% have scores ≥3 and <2, and 14% have scores of 4 or over.

Example 6—Combined Genetic and Clinical Risk Assessment—7 and 10 Polymorphism Panels

To further improve the method of the invention the inventors downloaded an updated results file on 8 Jan. 2021 from the UK Biobank. Eligible participants were active UK Biobank participants with a positive SARS-CoV-2 test result and who had SNP and hospital data available. Of the 47,990 UK Biobank participants with a SARS-CoV-2 test result available, 8,672 (18.1%) had a positive test result, and of these, 7,621 met the eligibility criteria.

The inventors used source of test result as a proxy for severity of disease, where inpatient results were considered severe disease (cases) and outpatient results were considered non-severe disease (controls). If a participant had more than one test result, they were classified as having severe disease if at least one of their results was from an inpatient setting. Of the 7,621 eligible participants, 2,205 were cases and 5,416 were controls.

The inventors identified a further 40 SNPs from the publicly available (release 4) results of the meta-analysis of non-hospitalised versus hospitalised cases of COVID-19 conducted by the COVID-19 Host Genetics Initiative consortium (COVID-19 Host Genetics Initiative (2020) and COVID-19 Host Genetics Initiative: results. 2020 accessed Jan. 7, 2020, at www.covid19hg.org/results). P<0.0001 was used as the threshold for loci selection and variants that were associated with hospitalisation in only one of the five studies included in the meta-analysis were removed. Variants that had a minor allele frequency of <0.01 and beta coefficients from −1 to 1 were then discarded (Dayem et al., 2018). Linkage disequilibrium pruning was performed using an r2 threshold of 0.5 against the 1000 Genomes European populations (CEU, TSI, FIN, GBR, IBS) representing the ethnicities of the submitted populations (Machiela et al., 2015). Where possible, SNP variants were chosen over insertion—deletion variants to facilitate laboratory validation testing. A further 12 SNPs were identified from publicly available meta-analysis of Covid-19 data (Pairo-Castineira et al., 2020).

The above identified SNPs were combined with the 64 identified in our original study to provide a test SNP panel of 116 SNPs.

To develop a new model to predict risk of severe COVID-19, the inventors used all of the available data and randomly divided it into a 70% training dataset and a 30% validation dataset (ensuring that it was balanced for origin of test result). Because the missing data is assumed to be missing at random (if not missing completely at random), a multiple imputation with 20 imputations was used to address the missing data for body mass index (linear regression) and the SNP data (predictive mean matching) for the development of the new model in the training dataset. To more closely reflect the availability of data in the real world, the inventors did not use imputed data in the validation dataset.

The clinical variables considered for inclusion in the new model were age, sex, BMI, ethnicity, ABO blood type and the following chronic health conditions: asthma, autoimmune disease (rheumatoid arthritis, lupus or psoriasis), haematological cancer, non-haematological cancer, cerebrovascular disease, diabetes, heart disease, hypertension, immunocompromised, kidney disease, liver disease and respiratory disease (excluding asthma). Dummy variables were used for the categorical classifications of age and ABO blood type.

The SNPs selected for the development of the new model came from three sources: (i) from Tables 2 to 4, (ii) the 40 SNPs newly selected from the (release 4) results of the COVID-19 Host Genetics Initiative meta-analysis of non-hospitalised versus hospitalised cases of COVID-191 2 and (iii) the 12 SNPs from the paper by Pairo-Castineira et al. (2020). The inventors used unadjusted logistic regression in the testing dataset to identify SNPS that were associated with risk of severe COVID-19 with P<0.05 (see Table 14).

Stata (version 16.1) was used for analyses; all statistical tests were two-sided and P<0.05 was considered nominally statistically significant.

TABLE 14 Informative polymorphisms assessed in Example 6. Position Reference Effect Chr SNP (GRCh37) Allele Frequency Allele Frequency OR 95% CI P 1 rs10873821 87628173 C 0.75 T 0.25 0.92 0.84, 1.02 0.10 1 rs112317747 239197542 T 0.97 C 0.03 1.26 1.00, 1.58 0.05 1 rs115492982 150271556 G 1.00 A 0.00 2.46 1.23, 4.91 0.01 1 rs12083278 31624029 G 0.29 C 0.71 1.04 0.95, 1.15 0.40 1 rs12745140 2998313 G 0.91 A 0.09 0.90 0.77, 1.06 0.20 1 rs17102023 46618634 A 1.00 G 0.00 1.33 0.63, 2.81 0.50 1 rs2224986 152684866 C 0.91 T 0.09 0.98 0.85, 1.14 0.80 1 rs2274122 36549664 G 0.20 A 0.80 0.97 0.88, 1.07 0.50 1 rs2765013 36374101 C 0.91 T 0.09 1.01 0.96, 1.26 0.20 1 rs74508649 192526317 C 1.00 T 0.00 1.04 0.47, 2.32 0.90 2 rs183569214 79895332 G 1.00 A 0.00 0.72 0.15, 3.45 0.70 2 rs2034831 182353446 A 0.94 C 0.06 1.22 1.03, 1.46 0.02 2 rs2270360 217524986 A 0.74 C 0.26 0.94 0.85, 1.04 0.20 2 rs6714112 36905013 C 0.86 A 0.14 1.02 0.90, 1.16 0.70 2 rs77764981 80029580 T 1.00 C 0.00 1.29 0.54, 3.10 0.60 3 rs10510749 46180416 C 0.91 T 0.09 0.99 0.85, 1.15 0.90 3 rs11385942 45876459 G 0.92 GA 0.08 1.16 1.00, 1.34 0.05 3 rs115102354 46222037 A 0.95 G 0.05 0.96 0.79, 1.16 0.70 3 rs12639224 45916222 C 0.73 T 0.27 1.02 0.93, 1.12 0.70 3 rs13062942 62936766 A 0.64 G 0.36 0.92 0.84, 1.01 0.09 3 rs13433997 46049765 T 0.88 C 0.12 1.10 0.97, 1.24 0.10 3 rs1504061 1093795 C 0.95 G 0.05 1.13 0.94, 1.36 0.20 3 rs1705826 3184653 C 0.63 G 0.37 1.03 0.94, 1.12 0.50 3 rs17317135 27188298 G 0.95 A 0.05 0.89 0.73, 1.09 0.30 3 rs1868132 125837737 C 0.90 T 0.10 1.02 0.89, 1.17 0.80 3 rs34901975 45916786 G 0.89 A 0.11 1.12 0.98, 1.27 0.09 3 rs35652899 45908514 C 0.93 G 0.07 1.17 1.00, 1.36 0.04 3 rs35896106 45841938 C 0.92 T 0.08 1.17 1.01, 1.35 0.04 3 rs6440031 141408691 A 0.08 G 0.92 0.94 0.80, 1.11 0.50 3 rs71325088 45862952 T 0.92 C 0.08 1.15 0.99, 1.33 0.07 3 rs71615437 46018781 A 0.92 G 0.08 1.12 0.97, 1.29 0.10 3 rs73064425 45901089 C 0.92 T 0.08 1.15 0.99, 1.33 0.07 3 rs76374459 45900634 G 0.94 C 0.06 1.20 1.02, 1.41 0.03 3 rs76488148 148718087 G 0.96 T 0.04 1.25 1.02, 1.52 0.03 4 rs112641600 112613026 C 0.89 T 0.11 0.83 0.72, 0.96 0.01 4 rs115162070 69705994 G 0.93 A 0.07 0.90 0.75, 1.07 0.20 4 rs11729561 106943200 T 0.92 C 0.08 0.96 0.82, 1.12 0.60 4 rs35540967 44418592 T 0.93 C 0.07 1.02 0.87, 1.19 0.80 4 rs3774881 5821877 T 0.84 C 0.16 0.91 0.81, 1.02 0.10 4 rs3774882 5821922 C 0.92 G 0.08 0.90 0.77, 1.06 0.20 4 rs6810404 27383278 C 0.51 A 0.49 0.97 0.89, 1.05 0.50 5 rs10039856 142252549 C 0.90 T 0.10 1.10 0.96, 1.26 0.20 5 rs111265173 171480160 C 1.00 T 0.00 0.97 0.35, 2.66 1.00 5 rs113791144 180237828 C 0.93 T 0.07 0.97 0.82, 1.15 0.70 5 rs2220543 173989338 T 0.71 A 0.29 1.04 0.94, 1.14 0.50 5 rs4240376 123950404 G 0.80 T 0.20 0.99 0.89, 1.10 0.80 5 rs4478338 169590905 T 0.92 G 0.08 1.08 0.93, 1.25 0.30 5 rs62377777 122832716 T 0.79 C 0.21 0.96 0.87, 1.07 0.50 6 rs10755709 12216966 A 0.70 G 0.30 1.11 1.01, 1.21 0.03 6 rs140247774 18015447 C 0.93 T 0.07 0.93 0.78, 1.10 0.40 6 rs143334143 31121426 G 0.93 A 0.07 1.00 0.85, 1.18 1.00 6 rs16873740 45704813 T 0.88 A 0.12 1.16 1.03, 1.32 0.02 6 rs3131294 32180146 A 0.13 G 0.87 1.00 0.88, 1.13 1.00 6 rs61611950 27604726 C 0.99 T 0.01 0.92 0.56, 1.51 0.80 6 rs6933436 6925195 A 0.71 C 0.29 1.01 0.92, 1.11 0.90 6 rs9380142 29798794 G 0.30 A 0.70 1.08 0.99, 1.19 0.09 6 rs9386484 106326754 T 0.76 A 0.24 0.95 0.85, 1.05 0.30 7 rs6967210 152960930 T 0.99 C 0.01 1.17 0.86, 1.59 0.30 8 rs10808999 38821327 A 0.13 G 0.87 1.01 0.89, 1.14 0.90 8 rs11779911 40181978 C 0.67 A 0.33 0.99 0.90, 1.09 0.90 8 rs118072448 16790149 T 0.92 C 0.08 0.82 0.70, 0.97 0.02 8 rs13282163 38897470 A 0.92 C 0.08 0.93 0.80, 1.09 0.40 8 rs2010843 74268198 T 0.47 C 0.53 1.04 0.96, 1.13 0.40 8 rs332040 8730488 G 0.53 A 0.47 1.00 0.92, 1.09 0.90 9 rs12236000 21131627 G 0.92 C 0.08 0.95 0.81, 1.11 0.50 9 rs3895472 4329170 T 0.08 C 0.92 1.04 0.88, 1.22 0.70 9 rs657152 136139265 C 0.63 A 0.37 0.95 0.87, 1.03 0.20 9 rs7027911 81158113 G 0.57 A 0.43 1.11 1.01, 1.21 0.02 9 rs71480372 27121456 A 0.66 T 0.34 0.98 0.90, 1.08 0.70 9 rs74790577 29688719 A 1.00 T 0.00 1.05 0.27, 4.03 0.90 10 rs10793436 44015051 G 0.68 T 0.32 0.95 0.86, 1.04 0.20 10 rs1441121 54100345 T 0.57 A 0.43 0.95 0.87, 1.03 0.20 10 rs1892429 37454397 A 0.84 G 0.16 0.99 0.88, 1.11 0.80 10 rs2091431 37277870 A 0.28 G 0.72 1.03 0.94, 1.14 0.50 10 rs5016035 123000638 T 0.51 G 0.49 1.00 0.91, 1.10 0.90 10 rs71481792 9030308 A 0.38 T 0.62 0.89 0.82, 0.97 0.01 11 rs10766439 2893867 A 0.37 G 0.63 0.97 0.89, 1.05 0.40 12 rs10735079 113380008 G 0.36 A 0.64 0.98 0.90, 1.07 0.60 12 rs11613792 8760610 A 0.85 G 0.15 1.01 0.88, 1.14 0.90 12 rs12823094 106624953 T 0.76 A 0.24 1.08 0.98, 1.19 0.10 12 rs6489867 113363550 C 0.36 T 0.64 0.98 0.90, 1.07 0.70 12 rs7397549 56084466 T 0.59 C 0.41 0.99 0.90, 1.09 0.90 13 rs12871414 74558505 C 0.74 T 0.26 0.95 0.86, 1.05 0.30 13 rs1984162 23658838 A 0.75 G 0.25 1.10 1.00, 1.21 0.05 13 rs2649134 63178476 C 0.97 T 0.03 0.93 0.72, 1.19 0.50 14 rs12587980 72934229 C 0.63 T 0.37 1.03 0.95, 1.13 0.40 14 rs144114696 77692036 G 1.00 A 0.00 2.53  0.51, 12.44 0.30 14 rs2238187 72908102 A 0.65 G 0.35 1.07 0.97, 1.17 0.20 15 rs12593288 33908103 C 0.80 T 0.20 0.91 0.82, 1.01 0.08 15 rs2229117 33916053 G 0.86 C 0.14 0.90 0.80, 1.02 0.10 15 rs74750712 48984345 T 1.00 G 0.00 1.33 0.65, 2.69 0.40 15 rs77055952 45858905 A 0.95 G 0.05 1.07 0.88, 1.29 0.50 16 rs145643452 49311043 G 0.99 A 0.01 1.03 0.61, 1.74 0.90 16 rs72779789 10579876 G 0.95 C 0.05 1.04 0.85, 1.26 0.70 16 rs72803978 78624025 A 0.94 G 0.06 0.88 0.74, 1.05 0.20 17 rs178840 29737612 G 0.75 A 0.25 0.93 0.84, 1.03 0.20 17 rs34761447 9170408 C 0.90 T 0.10 1.02 0.89, 1.18 0.80 17 rs9890316 80443309 G 0.69 A 0.31 1.01 0.92, 1.10 0.90 18 rs12958013 67208392 T 0.86 C 0.14 1.08 0.96, 1.22 0.20 18 rs142257532 30006171 T 0.97 C 0.03 1.01 0.78, 1.30 1.00 19 rs10411226 53333975 G 0.25 A 0.75 1.04 0.94, 1.16 0.40 19 rs11085727 10466123 C 0.72 T 0.28 1.06 0.96, 1.16 0.20 19 rs2109069 4719443 G 0.68 A 0.32 1.01 0.92, 1.10 0.80 19 rs60744406 44492164 A 0.41 G 0.59 1.02 0.94, 1.11 0.60 19 rs74956615 10427721 T 0.95 A 0.05 1.05 0.87, 1.27 0.60 19 rs8105499 32023957 C 0.70 A 0.30 0.98 0.90, 1.08 0.70 20 rs56259900 39389409 A 1.00 G 0.00 1.15 0.65, 2.04 0.60 20 rs76253189 60473717 C 0.99 G 0.01 1.01 0.72, 1.42 1.00 21 rs13050728 34615210 C 0.68 T 0.32 1.08 0.99, 1.18 0.10 21 rs2236757 34624917 G 0.70 A 0.30 1.06 0.97, 1.16 0.20 21 rs2252109 43080428 A 0.48 T 0.52 0.98 0.90, 1.07 0.70 21 rs75994231 44424444 C 0.98 T 0.02 1.06 0.79, 1.43 0.70 22 rs11090305 24407483 T 0.80 C 0.20 1.06 0.96, 1.18 0.20 22 rs5757427 22564734 T 0.65 A 0.35 0.96 0.88, 1.05 0.40 22 rs62220604 49677464 G 0.73 A 0.27 0.97 0.88, 1.07 0.50 22 rs7290963 22724951 G 0.55 T 0.45 1.00 0.92, 1.09 1.00

Development of New Model

The inventors used multivariable logistic regression in the multiple imputation training dataset to develop the new model to predict risk of severe COVID-19. The inventors began with a model that included all the clinical variables and the SNPs with unadjusted associations with severe COVID-19 and used backwards stepwise selection to develop the most parsimonious model. For the removed variables a final determination was made on their inclusion or exclusion by adding them one at a time to the parsimonious model. To directly compare the effect sizes of the variables in the final model, regardless of the scale on which they were measured, the odds per adjusted standard deviation was used. The intercept and beta coefficients from the new model to calculate the COVID-19 risk score was used for all eligible UK Biobank participants.

Model Performance

The inventors assessed the performance of the new model in the imputed development dataset and in the non-imputed validation dataset. The association between the risk score and severe COVID-19 was assessed using logistic regression to estimate the odds ratio per quintile of risk score. It was assessed model discrimination using the area under the receiver operating characteristic curve (AUC). For models that showed good discrimination, calibration was assessed using logistic regression of the log of the risk score to estimate the intercept and the slope (beta coefficient). An intercept close to 0 indicated good calibration, while an intercept less than 0 indicated overall overestimation of risk and an intercept greater than 0 indicated overall underestimation of risk. A slope of close to 1 indicated good dispersion with a slope of less than 1 indicating over-dispersion and slope of greater than 1 indicating under-dispersion.

The best performing tests are detailed below.

Risk Models

Three models were developed for assessing the risk of a human subject developing a severe response to a Coronavirus infection. In particular, the methods can be used to determine the probability the subject would require hospitalisation if infected with a Coronavirus. The first model is based solely on sex and age (referred to herein as the “age and sex model”), the second model (referred to herein as the “full model”) includes numerous clinical factors and genetic factors, whereas the third model (referred to herein as the “expanded model”) includes additional clinical factors and genetic factors to those in the full model.

Age and Sex Model

Inputs of the age and sex model are provided in Table 15 and the β-coefficients provided in Table 16.

TABLE 15 Age and Sex Model Product Inputs Clinical Risk Factor Input Acceptance TRF Question Age (years) Value 50-84 What is your age? Gender Male Male What is your gender? Female Female

TABLE 16 Age and Sex Model Risk Factors Variable Value β coefficient Age group 50-64 0 (years) 65-69 0.4694892 70-74 1.006561 75-79 1.435318 80-84 1.599188 Gender Female 0 Male 0.3911169

The long odds is calculated using: Log odds (LO)=−1.749562+Σ Clinical β coefficients.

The age and sex relative risk=e^(LO).

Age and sex probability=e^(LO)/(1+e^(LO)).

If any of the clinical factors are unknown, or the subject is unwilling to supply the relevant details, that factor(s) is assigned a β coefficient of 0.

Full Model

Inputs of the full model are provided in Table 17 and the β-coefficients provided in Tables 18 and 19.

TABLE 17 Full Model Product Inputs Clinical Risk Factor Input Acceptance TRF Question Age (years) Value 50-84 What is your age? Gender Male Male What is your gender? Female Female Ethnicity Caucasian All What is your ethnicity? Non- Caucasian Unknown Height (m) (m) All What is your height? Unknown Weight (kg) (kg) All What is your weight? Unknown Cerebro- No All Have you ever been diagnosed vascular Yes with cerebrovascular disease? disease Unknown Chronic kidney No All Have you ever been diagnosed disease Yes with chronic kidney disease? Unknown Diabetes No All Have you ever been diagnosed Yes with any type of diabetes? Unknown Haematological No All Have you ever been diagnosed cancer Yes with haematological cancer? Unknown Hypertension No All Have you ever been diagnosed Yes with hypertension? Unknown Non- No All Have you ever been diagnosed haematological Yes with another type of cancer? cancer Unknown Respiratory No All Have you ever been diagnosed disease Yes with a respiratory disease (excluding Unknown (excluding asthma)? asthma)

TABLE 18 Full Model Clinical Risk Factors Variable Value β coefficient Age group (years) 50-59 0 70-74 0.5747727 75-79 0.8243711 80-84 1.013973 Gender Female 0 Male 0.2444891 Ethnicity Caucasian 0 Other/Unknown 0.29311 Height (m) Weight (kg) ${10 \times {inverse}\mspace{14mu}{BMI}} = \frac{10 \times m^{2}}{kg}$ $\frac{10 \times m^{2}}{kg}$ −1.602056 Cerebrovascular disease No 0 Yes 0.4041337 Chronic kidney disease No 0 Yes 0.6938494 Diabetes No 0 Yes 0.4297612 Haematological cancer No 0 Yes 1.003877 Hypertension No 0 Yes 0.2922307 Non-haematological cancer No 0 Yes 0.2558464 Respiratory disease No 0 (excluding asthma) Yes 1.173753

TABLE 19 Full Model SNP Risk Alleles SNPs Risk Allele No of Risk Alleles β coefficient rs10755709 G 0, 1, or 2 0.124239 rs112317747 C 0, 1, or 2 0.2737487 rs112641600 T 0, 1, or 2 −0.2362513 rs118072448 C 0, 1, or 2 −0.1995879 rs2034831 C 0, 1, or 2 0.2371955 rs7027911 A 0, 1, or 2 0.1019074 rs71481792 T 0, 1, or 2 −0.1058025

The SNP risk factor (SRF) is determined using: (SRF)=Σ (No of Risk Alleles×SNP β coefficient).

The long odds is calculated using: Log odds (LO)=−1.36523+SRF+Σ Clinical β coefficients.

The age and sex relative risk=e^(LO).

Age and sex probability=e^(LO)/(1+e^(LO)).

If any of the clinical factors are unknown, or the subject is unwilling to supply the relevant details, that factor(s) is assigned a β coefficient of 0.

Expanded Model

Inputs of the expanded model are provided in Table 20 and the β-coefficients provided in Tables 21 and 22.

TABLE 20 Expanded Model Product Inputs Clinical Risk Factor Input Acceptance TRF Question Age (years) Value 50-84 What is your age? Gender Male Male What is your gender? Female Female Ethnicity Caucasian All What is your ethnicity? Non- Caucasian Unknown Blood Type O All What is your blood type? A B AB Unknown Height (m) (m) All What is your height? Unknown Weight (kg) (kg) All What is your weight? Unknown Cerebro- No All Have you ever been diagnosed vascular Yes with cerebrovascular disease? disease Unknown Chronic kidney No All Have you ever been diagnosed disease Yes with chronic kidney disease? Unknown Diabetes No All Have you ever been diagnosed Yes with any type of diabetes? Unknown Haematological No All Have you ever been diagnosed cancer Yes with haematological cancer? Unknown Hypertension No All Have you ever been diagnosed Yes with hypertension? Unknown Immuno- No All Have you ever been diagnosed compromised Yes with an immuno compromised disease Unknown disease? Liver disease No All Have you ever been diagnosed Yes with a liver disease? Unknown Non- No All Have you ever been diagnosed haematological Yes with another type of cancer? cancer Unknown Respiratory No All Have you ever been diagnosed disease Yes with a respiratory disease (excluding Unknown (excluding asthma)? asthma)

TABLE 21 Exapnded Model Clinical and SNP Risk Factors Variable Value β coefficient Age group (years) 50-64 0 65-69 0.1677566 70-74 0.6352682 75-79 0.8940548 80-84 1.082477 Gender Female 0 Male 0.2418454 Ethnicity Caucasian 0 Other/Unknown 0.2967777 Blood Type O 0 A 0 B 0 AB −0.229737 Height (m) Weight (kg) ${10 \times {inverse}\mspace{14mu}{BMI}} = \frac{10 \times m^{2}}{kg}$ $\frac{10 \times m^{2}}{kg}$ −1.560943 Cerebrovascular disease No 0 Yes 0.3950113 Chronic kidney disease No 0 Yes 0.6650257 Diabetes No 0 Yes 0.4126633 Haematological cancer No 0 Yes 1.001079 Hypertension No 0 Yes 0.2640989 Immunocompromised disease No 0 Yes 0.6033541 Liver disease No 0 Yes 0.2301902 Non-haematological cancer No 0 Yes 0.2381579 Respiratory disease No 0 (excluding asthma) Yes 1.148496

TABLE 22 Expanded Model SNP Risk Alleles SNPs Risk Allele No of Risk Alleles β coefficient rs10755709 G 0, 1, or 2 0.1231766 rs112317747 C 0, 1, or 2 0.2576692 rs112641600 T 0, 1, or 2 −0.2384001 rs115492982 A 0, 1, or 2 0.4163575 rs118072448 C 0, 1, or 2 −0.1965609 rs1984162 A 0, 1, or 2 0.1034362 rs2034831 C 0, 1, or 2 0.2414792 rs7027911 A 0, 1, or 2 0.0998459 rs71481792 T 0, 1, or 2 −0.1032044

The SNP risk factor (SRF) is determined using: (SRF)=Σ (No of Risk Alleles×SNP β coefficient).

The long odds is calculated using: Log odds (LO)=−1.469939+SRF+Σ Clinical β coefficients.

The age and sex relative risk=e^(LO).

Age and sex probability=e^(LO)/(1+e^(LO)).

If any of the clinical factors are unknown, or the subject is unwilling to supply the relevant details, that factor(s) is assigned a β coefficient of 0.

SUMMARY

In terms of discrimination between cases and controls, the age and sex model had an AUC of 0.671 (95% CI=0.646, 0.696) but the full model with an AUC of 0.732 (95% CI=0.708, 0.756) was a substantial improvement (χ2=41.23, df=1, P<0.001). The receiver operating characteristic curves for both models are shown in FIG. 4.

The models were well calibrated with no evidence of overall overestimation or underestimation for the age and sex model (α=−0.02; 95% CI=−0.18, 0.13; P=0.7) or the full model (α=−0.08; 95% CI=−0.21, 0.05; P=0.3). There was also no evidence of under or over dispersion for the age and sex model (β=0.96, 95% CI=0.81, 1.10, P=0.6) and for the full model (β=0.90, 95% CI=0.80, 1.00, P=0.06). Calibration plots for both models are shown in FIG. 5.

The inventors calculated the probability of severe COVID-19 for all UK Biobank participants who met our eligibility criteria for this study; the distributions are shown in FIG. 6. Using the age and sex model, the mean probability was 0.32 (SD=0.13) and ranged from a minimum of 0.15 to a maximum of 0.56. Using the full model, the mean probability was 0.27 (SD=0.16) and the range was from 0.04 to 0.98, a much wider range than for the age and sex model.

The expanded model provided a slight improvement in discrimination in this dataset (Table 23).

TABLE 23 Test Performances. Model AUC 95% Confidence interval Age and Sex 0.6755 0.65948-0.69160 Full Model 0.7512 0.73653-0.76673 Expanded Model 0.7524 0.73730-0.76749

Example 7—Combined Genetic and Clinical Risk Assessment—Under 50 Years of Age

The algorithm to calculate the risk of developing severe Covid-19 has been modified to enable a risk calculation to be provided for patients aged 18-85 years (previously 50-85 years). More specifically, the look-up tables providing the age-related risk values have been modified to include three additional values for the following age ranges: 18-29, 30-39, 40-49 (Tables 24).

For people aged under 50 years, the probability of severe disease is adjusted using data on risk of hospitalization due to Covid-19 which were obtained from the United States Centers for Disease Control and Prevention (www.cdc.gov).

The SNPs analysed, and the methods used for analysis, are the same as used in Example 6.

TABLE 24 Test Performances. Variable Value β coefficient Age group (years) 18-29 −1.3111 30-39 −0.8348 40-49 −0.4038 50-69 0 70-74 0.5747727 75-79 0.8243711 80-84 1.013973 Gender Female 0 Male 0.2444891 Ethnicity Caucasian 0 Other/Unknown 0.29311 Height (m) Value Used in in BMI calculation Weight (kg) Value Used in in BMI calculation ${10 \times {inverse}\mspace{14mu}{BMI}} = \frac{10 \times m^{2}}{kg}$ $\frac{10 \times m^{2}}{kg}$ −1.602056 Cerebrovascular disease No 0 Yes 0.4041337 Chronic kidney disease No 0 Yes 0.6938494 Diabetes No 0 Yes 0.4297612 Haematological cancer No 0 Yes 1.003877 Hypertension No 0 Yes 0.2922307 Non-haematological cancer No 0 Yes 0.2558464 Respiratory disease No 0 (excluding asthma) Yes 1.173753

The present application claims priority from AU 2020901739 filed 27 May 2020, AU 2020902052 filed 19 Jun. 2020, AU 2020903536 filed 30 Sep. 2020, and AU 2021900392 filed 17 Feb. 2021, the entire contents of each of which are incorporated herein by reference.

It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

All publications discussed and/or referenced herein are incorporated herein in their entirety.

Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.

REFERENCES

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1-12. (canceled)
 13. A method for determining the probability a human subject will develop a severe response to a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the method comprising: i) performing a clinical risk assessment of the human subject wherein the clinical risk assessment comprises: (a) obtaining information from the subject on age, gender, race/ethnicity, height, weight, does the human have or has had an cerebrovascular disease, does the human have or has had a chronic kidney disease, does the human have or has had diabetes, does the human have or has had an haematological cancer, does the human have or has had hypertension, does the human have or has had an non-haematological cancer, and does the human have or has had a respiratory disease (other than asthma); and (b) assigning a clinical β coefficient based on each piece of information obtained from the subject in step (i)(a); ii) determining the Log Odds (LO) using the following formula: LO=X+Σ Clinical β coefficients  wherein X is −1.8 to −0.8; iii) determining the probability the subject will develop a severe response to a SARS-CoV-2 infection using the following formula: e ^(LO)/(1+e ^(LO)), which is then multiplied by
 100. 14. The method of claim 13, wherein in step (ii) X is −1.36523.
 15. The method of claim 13, wherein the subject is between 18 and 84 years of age and in the clinical risk assessment a) a β coefficient of −1.3111 is assigned if the subject is between 18 and 29 years of age; b) a β coefficient of −0.8348 is assigned if the subject is between 30 and 39 years of age; c) a β coefficient of −0.4038 is assigned if the subject is between 40 and 49 years of age; d) a β coefficient of 0.5747727 is assigned if the subject is between 70 and 74 years of age; e) a β coefficient of 0.8243711 is assigned if the subject is between 75 and 79 years of age; f) a β coefficient of 1.013973 is assigned if the subject is between 80 and 84 years of age; g) a β coefficient of 0.2444891 is assigned if the subject is male; h) a β coefficient of 0.29311 is assigned if the subject is an ethnicity other than Caucasian; i) the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m2) divided by kg, which is multiplied by −1.602056 to provide the β coefficient to be assigned; j) a β coefficient of 0.4041337 is assigned if the subject has ever been diagnosed as having a cerebrovascular disease; k) a β coefficient of 0.6938494 is assigned if the subject has ever been diagnosed as having a chronic kidney disease; l) a β coefficient of 0.4297612 is assigned if the subject has ever been diagnosed as having diabetes; m) a β coefficient of 1.003877 is assigned if the subject has ever been diagnosed as having haematological cancer; n) a β coefficient of 0.2922307 is assigned if the subject has ever been diagnosed as having hypertension; o) a β coefficient of 0.2558464 is assigned if the subject has ever been diagnosed as having a non-haematological cancer; and p) a β coefficient of 1.173753 is assigned if the subject has ever been diagnosed as having a respiratory disease (other than asthma).
 16. The method of claim 14, wherein the subject is between 18 and 84 years of age and in the clinical risk assessment a) a β coefficient of −1.3111 is assigned if the subject is between 18 and 29 years of age; b) a β coefficient of −0.8348 is assigned if the subject is between 30 and 39 years of age; c) a β coefficient of −0.4038 is assigned if the subject is between 40 and 49 years of age; d) a β coefficient of 0.5747727 is assigned if the subject is between 70 and 74 years of age; e) a β coefficient of 0.8243711 is assigned if the subject is between 75 and 79 years of age; f) a β coefficient of 1.013973 is assigned if the subject is between 80 and 84 years of age; g) a β coefficient of 0.2444891 is assigned if the subject is male; h) a β coefficient of 0.29311 is assigned if the subject is an ethnicity other than Caucasian; i) the subjects height (in metres (m)) and weight (in kilograms (kg)) is applied to the formula: (10 times m2) divided by kg, which is multiplied by −1.602056 to provide the β coefficient to be assigned; j) a β coefficient of 0.4041337 is assigned if the subject has ever been diagnosed as having a cerebrovascular disease; k) a β coefficient of 0.6938494 is assigned if the subject has ever been diagnosed as having a chronic kidney disease; l) a β coefficient of 0.4297612 is assigned if the subject has ever been diagnosed as having diabetes; m) a β coefficient of 1.003877 is assigned if the subject has ever been diagnosed as having haematological cancer; n) a β coefficient of 0.2922307 is assigned if the subject has ever been diagnosed as having hypertension; o) a β coefficient of 0.2558464 is assigned if the subject has ever been diagnosed as having a non-haematological cancer; and p) a β coefficient of 1.173753 is assigned if the subject has ever been diagnosed as having a respiratory disease (other than asthma).
 17. The method of claim 13, further comprising comparing the Log Odds (LO) to a predetermined threshold, wherein if the score is at, or above, the threshold the subject is assessed at being at risk of developing a severe response to a Coronavirus infection.
 18. The method of claim 14, further comprising comparing the Log Odds (LO) to a predetermined threshold, wherein if the score is at, or above, the threshold the subject is assessed at being at risk of developing a severe response to a Coronavirus infection.
 19. The method of claim 15, further comprising comparing the Log Odds (LO) to a predetermined threshold, wherein if the score is at, or above, the threshold the subject is assessed at being at risk of developing a severe response to a Coronavirus infection. 